diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/1_Pooling/config.json b/1_Pooling/config.json new file mode 100644 index 0000000000000000000000000000000000000000..63a38bc5f5604fc547f58915f0e16e60d6306534 --- /dev/null +++ b/1_Pooling/config.json @@ -0,0 +1,10 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": true, + "pooling_mode_mean_tokens": false, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false, + "pooling_mode_weightedmean_tokens": false, + "pooling_mode_lasttoken": false, + "include_prompt": true +} \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..512e17aeb4de1406a61fe035fa56e719c6d2aeed --- /dev/null +++ b/README.md @@ -0,0 +1,670 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- dense +- generated_from_trainer +- dataset_size:72349 +- loss:MultipleNegativesRankingLoss +base_model: Snowflake/snowflake-arctic-embed-m-v2.0 +widget: +- source_sentence: Userin kann die eingetragene AU nicht löschen. + sentences: + - "Userin muss über das Drei Punkte System gehen und dann über Abwesenheitszeitraum\ + \ eintragen und als Art Einträge löschen auswählen.\r\nMit Userin die AU zusammen\ + \ gelöscht." + - Hier muss bei allen Kindern der Haken bei "förderfähig" in der BI gesetzt werden. + - "Userin an ihren Träger verwiesen. \r\nUserin erklärt, dass die AWO keinen Support\ + \ über uns hat." +- source_sentence: User möchte EL für BV freischalten. + sentences: + - Userin hatte in der Beschäftigung zu wenige Stunden für den bestimmten Zeitraum + hinterlegt. Sie muss passend zu der Erstattung auch passende Stunden hinterlegen. + - Anwenderin musst den Filter weiter zurückstellen. + - Die Rolle Einrichtung kann keinen Zugriff dazu erhalten. Das ist so konzeptionell + vom LJA so festgesetzt. +- source_sentence: Userin kann EVN nicht freigeben. Sie wird gebeten, dass sie die + Monatsdaten neu erstellt und freigibt. Das System macht dies aber nicht. Sie bekommt + auch keine Fehlermeldung. + sentences: + - Kidz hatte zum Zeitpunkt des Anrufs eine Störung, die vermutlich zu diesem Problem + geführt hat. Userin leider nicht mehr erreicht, daher wird der Anruf geschlossen. + - Nein, wenn nur auf der kitaplus-Verwaltungsseite, wird als Wunsch für die GAPP + weitergegeben. + - Ja im Berichtsgenerator kann sie sich eine entsprechende Liste ziehen +- source_sentence: Er kann einen Antrag auf Personalausnahme nicht freigeben. Trotz + Setzung der Haken über Beschäftigungsinformationen können die Daten nicht gespeichert + werden. + sentences: + - Es handelt sich um ein lokales Problem. Die Seite baut sich nach dem Löschen + mit der aktualisierten Zahl nicht automatisch wieder auf. Durch die Taste F5 wird + die Seite neu geladen. + - Sie kann Vertretung wählen oder ggf eine andere und die Qualifikation muss die + Mitarbeiterin ihr nennen. Sonst kann sie dazu beim Landesamt nachfragen, da inhaltliche + Fragen + - Er speichert diese über Einrichtungsdaten speichern. Danach konnte der Antrag + freigegeben werden. +- source_sentence: "Ein Vater taucht nicht auf bei den Eltern im Elternbeirat \r\n\ + \r\nAußerdem auf die Kinder mit archivierten Angehörigen hingewiesen und ihr gezeigt" + sentences: + - "1. Vorlage da. Userin auch gezeigt wie sie die verwanden kann\r\n2. Als Wunsch\ + \ weitergegeben." + - In der Kinderliste haben Kinder gefehlt. Userin muss die Daten in der Kinderliste + hinterlegen. + - Weil er keinen Zugang zur EAPP hat, Außerdem auf die Kinder mit archivierten Angehörigen + hingewiesen und ihr gezeigt wie sie das lösen kann +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@3 +- cosine_accuracy@5 +- cosine_accuracy@10 +- cosine_precision@1 +- cosine_precision@3 +- cosine_precision@5 +- cosine_precision@10 +- cosine_recall@1 +- cosine_recall@3 +- cosine_recall@5 +- cosine_recall@10 +- cosine_ndcg@10 +- cosine_mrr@10 +- cosine_map@100 +model-index: +- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0 + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: Snowflake/snowflake arctic embed m v2.0 + type: Snowflake/snowflake-arctic-embed-m-v2.0 + metrics: + - type: cosine_accuracy@1 + value: 0.11594202898550725 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.6376811594202898 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.7536231884057971 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.8840579710144928 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.11594202898550725 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.33816425120772947 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.33623188405797105 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.2710144927536232 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.01515151515151515 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.08134210205821459 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.12813742097629566 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.19648586707410237 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.29104944190834786 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4015067862893949 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.19346359069331198 + name: Cosine Map@100 +--- + +# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0 + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) +- **Maximum Sequence Length:** 8192 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'GteModel'}) + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("BjarneNPO-01_09_2025_15_09_37") +# Run inference +queries = [ + "Ein Vater taucht nicht auf bei den Eltern im Elternbeirat \r\n\r\nAu\u00dferdem auf die Kinder mit archivierten Angeh\u00f6rigen hingewiesen und ihr gezeigt", +] +documents = [ + 'Weil er keinen Zugang zur EAPP hat, Außerdem auf die Kinder mit archivierten Angehörigen hingewiesen und ihr gezeigt wie sie das lösen kann', + '1. Vorlage da. Userin auch gezeigt wie sie die verwanden kann\r\n2. Als Wunsch weitergegeben.', + 'In der Kinderliste haben Kinder gefehlt. Userin muss die Daten in der Kinderliste hinterlegen.', +] +query_embeddings = model.encode_query(queries) +document_embeddings = model.encode_document(documents) +print(query_embeddings.shape, document_embeddings.shape) +# [1, 768] [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(query_embeddings, document_embeddings) +print(similarities) +# tensor([[ 0.4964, 0.1370, -0.0431]]) +``` + + + + + + + +## Evaluation + +### Metrics + +#### Information Retrieval + +* Dataset: `Snowflake/snowflake-arctic-embed-m-v2.0` +* Evaluated with scripts.InformationRetrievalEvaluatorCustom.InformationRetrievalEvaluatorCustom with these parameters: + ```json + { + "query_prompt_name": "query", + "corpus_prompt_name": "query" + } + ``` + +| Metric | Value | +|:--------------------|:----------| +| cosine_accuracy@1 | 0.1159 | +| cosine_accuracy@3 | 0.6377 | +| cosine_accuracy@5 | 0.7536 | +| cosine_accuracy@10 | 0.8841 | +| cosine_precision@1 | 0.1159 | +| cosine_precision@3 | 0.3382 | +| cosine_precision@5 | 0.3362 | +| cosine_precision@10 | 0.271 | +| cosine_recall@1 | 0.0152 | +| cosine_recall@3 | 0.0813 | +| cosine_recall@5 | 0.1281 | +| cosine_recall@10 | 0.1965 | +| **cosine_ndcg@10** | **0.291** | +| cosine_mrr@10 | 0.4015 | +| cosine_map@100 | 0.1935 | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 72,349 training samples +* Columns: query and answer +* Approximate statistics based on the first 1000 samples: + | | query | answer | + |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | query | answer | + |:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Nun ist die Monatsmeldung erfolgt, aber rote Ausrufezeichen tauchen auf. | Userin an das JA verwiesen, diese müssten ihr die Schloss-Monate zur Überarbeitung im Kibiz.web zurückgeben. Userin dazu empfohlen, die Kinder die nicht in kitaplus sind, aber in Kibiz.web - im KiBiz.web zu entfernen, wenn diese nicht vorhanden sind. | + | Die Feiertage in den Stammdaten stimmen nicht. | Es besteht bereits ein Ticket dafür. | + | Abrechnung kann nicht final freigegeben werden, es wird aber keiner Fehlermeldung angeziegt | im Hintergrund ist eine Fehlermeldung zu sehen. An Entwickler weitergeleitet.
Korrektur vorgenommen. | +* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim", + "gather_across_devices": false + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: epoch +- `per_device_train_batch_size`: 32 +- `per_device_eval_batch_size`: 32 +- `gradient_accumulation_steps`: 4 +- `learning_rate`: 4e-05 +- `lr_scheduler_type`: cosine +- `warmup_ratio`: 0.08 +- `bf16`: True +- `tf32`: True +- `load_best_model_at_end`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: epoch +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 32 +- `per_device_eval_batch_size`: 32 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 4 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 4e-05 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 3 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.08 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: True +- `fp16`: False +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: True +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: False +- `dataloader_num_workers`: 0 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: True +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch_fused +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: None +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `hub_revision`: None +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `liger_kernel_config`: None +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional +- `router_mapping`: {} +- `learning_rate_mapping`: {} + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | Snowflake/snowflake-arctic-embed-m-v2.0_cosine_ndcg@10 | +|:-------:|:--------:|:-------------:|:------------------------------------------------------:| +| 0.0177 | 10 | 2.222 | - | +| 0.0354 | 20 | 2.1657 | - | +| 0.0531 | 30 | 1.9611 | - | +| 0.0708 | 40 | 1.9064 | - | +| 0.0885 | 50 | 1.7658 | - | +| 0.1061 | 60 | 1.6493 | - | +| 0.1238 | 70 | 1.7006 | - | +| 0.1415 | 80 | 1.5747 | - | +| 0.1592 | 90 | 1.4265 | - | +| 0.1769 | 100 | 1.3506 | - | +| 0.1946 | 110 | 1.349 | - | +| 0.2123 | 120 | 1.2742 | - | +| 0.2300 | 130 | 1.1629 | - | +| 0.2477 | 140 | 1.173 | - | +| 0.2654 | 150 | 1.1773 | - | +| 0.2831 | 160 | 1.1101 | - | +| 0.3008 | 170 | 1.0419 | - | +| 0.3184 | 180 | 1.1084 | - | +| 0.3361 | 190 | 1.0667 | - | +| 0.3538 | 200 | 1.0174 | - | +| 0.3715 | 210 | 1.0776 | - | +| 0.3892 | 220 | 0.9883 | - | +| 0.4069 | 230 | 0.9751 | - | +| 0.4246 | 240 | 0.9467 | - | +| 0.4423 | 250 | 0.8335 | - | +| 0.4600 | 260 | 0.9196 | - | +| 0.4777 | 270 | 0.9604 | - | +| 0.4954 | 280 | 0.9193 | - | +| 0.5130 | 290 | 0.8227 | - | +| 0.5307 | 300 | 0.8538 | - | +| 0.5484 | 310 | 0.867 | - | +| 0.5661 | 320 | 0.8176 | - | +| 0.5838 | 330 | 0.8119 | - | +| 0.6015 | 340 | 0.8154 | - | +| 0.6192 | 350 | 0.8091 | - | +| 0.6369 | 360 | 0.7533 | - | +| 0.6546 | 370 | 0.7948 | - | +| 0.6723 | 380 | 0.792 | - | +| 0.6900 | 390 | 0.7766 | - | +| 0.7077 | 400 | 0.7901 | - | +| 0.7253 | 410 | 0.7517 | - | +| 0.7430 | 420 | 0.7825 | - | +| 0.7607 | 430 | 0.7635 | - | +| 0.7784 | 440 | 0.8115 | - | +| 0.7961 | 450 | 0.7359 | - | +| 0.8138 | 460 | 0.7824 | - | +| 0.8315 | 470 | 0.7623 | - | +| 0.8492 | 480 | 0.7299 | - | +| 0.8669 | 490 | 0.7406 | - | +| 0.8846 | 500 | 0.7614 | - | +| 0.9023 | 510 | 0.6883 | - | +| 0.9199 | 520 | 0.6846 | - | +| 0.9376 | 530 | 0.6769 | - | +| 0.9553 | 540 | 0.7502 | - | +| 0.9730 | 550 | 0.7281 | - | +| 0.9907 | 560 | 0.6141 | - | +| 1.0 | 566 | - | 0.2907 | +| 1.0071 | 570 | 0.6137 | - | +| 1.0248 | 580 | 0.5913 | - | +| 1.0425 | 590 | 0.5994 | - | +| 1.0602 | 600 | 0.5915 | - | +| 1.0778 | 610 | 0.594 | - | +| 1.0955 | 620 | 0.576 | - | +| 1.1132 | 630 | 0.5402 | - | +| 1.1309 | 640 | 0.5558 | - | +| 1.1486 | 650 | 0.5526 | - | +| 1.1663 | 660 | 0.5585 | - | +| 1.1840 | 670 | 0.5472 | - | +| 1.2017 | 680 | 0.5427 | - | +| 1.2194 | 690 | 0.5229 | - | +| 1.2371 | 700 | 0.5307 | - | +| 1.2548 | 710 | 0.5319 | - | +| 1.2724 | 720 | 0.5614 | - | +| 1.2901 | 730 | 0.5825 | - | +| 1.3078 | 740 | 0.5405 | - | +| 1.3255 | 750 | 0.5867 | - | +| 1.3432 | 760 | 0.57 | - | +| 1.3609 | 770 | 0.5272 | - | +| 1.3786 | 780 | 0.5566 | - | +| 1.3963 | 790 | 0.5371 | - | +| 1.4140 | 800 | 0.5084 | - | +| 1.4317 | 810 | 0.4834 | - | +| 1.4494 | 820 | 0.5608 | - | +| 1.4670 | 830 | 0.5439 | - | +| 1.4847 | 840 | 0.5198 | - | +| 1.5024 | 850 | 0.5187 | - | +| 1.5201 | 860 | 0.5295 | - | +| 1.5378 | 870 | 0.5075 | - | +| 1.5555 | 880 | 0.5612 | - | +| 1.5732 | 890 | 0.4302 | - | +| 1.5909 | 900 | 0.5036 | - | +| 1.6086 | 910 | 0.5535 | - | +| 1.6263 | 920 | 0.5629 | - | +| 1.6440 | 930 | 0.4835 | - | +| 1.6617 | 940 | 0.5806 | - | +| 1.6793 | 950 | 0.5547 | - | +| 1.6970 | 960 | 0.5008 | - | +| 1.7147 | 970 | 0.5518 | - | +| 1.7324 | 980 | 0.4992 | - | +| 1.7501 | 990 | 0.4895 | - | +| 1.7678 | 1000 | 0.4911 | - | +| 1.7855 | 1010 | 0.4759 | - | +| 1.8032 | 1020 | 0.5082 | - | +| 1.8209 | 1030 | 0.5329 | - | +| 1.8386 | 1040 | 0.468 | - | +| 1.8563 | 1050 | 0.4719 | - | +| 1.8739 | 1060 | 0.4937 | - | +| 1.8916 | 1070 | 0.4628 | - | +| 1.9093 | 1080 | 0.4668 | - | +| 1.9270 | 1090 | 0.4645 | - | +| 1.9447 | 1100 | 0.4933 | - | +| 1.9624 | 1110 | 0.5119 | - | +| 1.9801 | 1120 | 0.5162 | - | +| 1.9978 | 1130 | 0.4628 | - | +| **2.0** | **1132** | **-** | **0.2949** | +| 2.0142 | 1140 | 0.406 | - | +| 2.0318 | 1150 | 0.384 | - | +| 2.0495 | 1160 | 0.4111 | - | +| 2.0672 | 1170 | 0.3909 | - | +| 2.0849 | 1180 | 0.4097 | - | +| 2.1026 | 1190 | 0.4208 | - | +| 2.1203 | 1200 | 0.4042 | - | +| 2.1380 | 1210 | 0.3715 | - | +| 2.1557 | 1220 | 0.4217 | - | +| 2.1734 | 1230 | 0.381 | - | +| 2.1911 | 1240 | 0.3838 | - | +| 2.2088 | 1250 | 0.3797 | - | +| 2.2264 | 1260 | 0.3548 | - | +| 2.2441 | 1270 | 0.4092 | - | +| 2.2618 | 1280 | 0.4176 | - | +| 2.2795 | 1290 | 0.4105 | - | +| 2.2972 | 1300 | 0.3414 | - | +| 2.3149 | 1310 | 0.3782 | - | +| 2.3326 | 1320 | 0.3626 | - | +| 2.3503 | 1330 | 0.3897 | - | +| 2.3680 | 1340 | 0.4173 | - | +| 2.3857 | 1350 | 0.376 | - | +| 2.4034 | 1360 | 0.37 | - | +| 2.4211 | 1370 | 0.3827 | - | +| 2.4387 | 1380 | 0.3509 | - | +| 2.4564 | 1390 | 0.3725 | - | +| 2.4741 | 1400 | 0.3447 | - | +| 2.4918 | 1410 | 0.3882 | - | +| 2.5095 | 1420 | 0.3764 | - | +| 2.5272 | 1430 | 0.408 | - | +| 2.5449 | 1440 | 0.384 | - | +| 2.5626 | 1450 | 0.386 | - | +| 2.5803 | 1460 | 0.3892 | - | +| 2.5980 | 1470 | 0.3337 | - | +| 2.6157 | 1480 | 0.4048 | - | +| 2.6333 | 1490 | 0.3788 | - | +| 2.6510 | 1500 | 0.3814 | - | +| 2.6687 | 1510 | 0.3978 | - | +| 2.6864 | 1520 | 0.3505 | - | +| 2.7041 | 1530 | 0.4267 | - | +| 2.7218 | 1540 | 0.3549 | - | +| 2.7395 | 1550 | 0.3723 | - | +| 2.7572 | 1560 | 0.3883 | - | +| 2.7749 | 1570 | 0.3714 | - | +| 2.7926 | 1580 | 0.3275 | - | +| 2.8103 | 1590 | 0.3672 | - | +| 2.8280 | 1600 | 0.3915 | - | +| 2.8456 | 1610 | 0.3476 | - | +| 2.8633 | 1620 | 0.3962 | - | +| 2.8810 | 1630 | 0.4102 | - | +| 2.8987 | 1640 | 0.3944 | - | +| 2.9164 | 1650 | 0.4045 | - | +| 2.9341 | 1660 | 0.4133 | - | +| 2.9518 | 1670 | 0.3768 | - | +| 2.9695 | 1680 | 0.3845 | - | +| 2.9872 | 1690 | 0.4359 | - | +| 3.0 | 1698 | - | 0.2910 | + +* The bold row denotes the saved checkpoint. +
+ +### Framework Versions +- Python: 3.10.11 +- Sentence Transformers: 5.1.0 +- Transformers: 4.55.2 +- PyTorch: 2.8.0+cu129 +- Accelerate: 1.10.0 +- Datasets: 3.6.0 +- Tokenizers: 0.21.4 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### MultipleNegativesRankingLoss +```bibtex +@misc{henderson2017efficient, + title={Efficient Natural Language Response Suggestion for Smart Reply}, + author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, + year={2017}, + eprint={1705.00652}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + + + + + + \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..20a95b39c75af0012054851b413a6ee25ac9e3f8 --- /dev/null +++ b/config.json @@ -0,0 +1,38 @@ +{ + "architectures": [ + "GteModel" + ], + "attention_probs_dropout_prob": 0.0, + "auto_map": { + "AutoConfig": "configuration_hf_alibaba_nlp_gte.GteConfig", + "AutoModel": "modeling_hf_alibaba_nlp_gte.GteModel" + }, + "classifier_dropout": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "layer_norm_type": "layer_norm", + "logn_attention_clip1": false, + "logn_attention_scale": false, + "matryoshka_dimensions": [ + 256 + ], + "max_position_embeddings": 8192, + "model_type": "gte", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pack_qkv": true, + "pad_token_id": 1, + "position_embedding_type": "rope", + "rope_scaling": null, + "rope_theta": 160000, + "torch_dtype": "float32", + "transformers_version": "4.55.2", + "type_vocab_size": 1, + "unpad_inputs": "true", + "use_memory_efficient_attention": "true", + "vocab_size": 250048 +} diff --git a/config_sentence_transformers.json b/config_sentence_transformers.json new file mode 100644 index 0000000000000000000000000000000000000000..1e186845822f16849aec838568917d0e06d01726 --- /dev/null +++ b/config_sentence_transformers.json @@ -0,0 +1,14 @@ +{ + "__version__": { + "sentence_transformers": "5.1.0", + "transformers": "4.55.2", + "pytorch": "2.8.0+cu129" + }, + "prompts": { + "query": "query: ", + "document": "" + }, + "default_prompt_name": null, + "model_type": "SentenceTransformer", + "similarity_fn_name": "cosine" +} \ No newline at end of file diff --git a/configuration_hf_alibaba_nlp_gte.py b/configuration_hf_alibaba_nlp_gte.py new file mode 100644 index 0000000000000000000000000000000000000000..d816ed663a58404f966fe322cd113ac39a957686 --- /dev/null +++ b/configuration_hf_alibaba_nlp_gte.py @@ -0,0 +1,145 @@ +# coding=utf-8 +# Copyright 2024 The GTE Team Authors and Alibaba Group. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" GTE model configuration""" +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class GteConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to + instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the NEW + [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + max_position_embeddings (`int`, *optional*, defaults to 512): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + type_vocab_size (`int`, *optional*, defaults to 2): + The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`]. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + position_embedding_type (`str`, *optional*, defaults to `"rope"`): + Type of position embedding. Choose one of `"absolute"`, `"rope"`. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling + strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is + `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + classifier_dropout (`float`, *optional*): + The dropout ratio for the classification head. + + Examples: + + ```python + >>> from transformers import NewConfig, NewModel + + >>> # Initializing a NEW izhx/new-base-en style configuration + >>> configuration = NewConfig() + + >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration + >>> model = NewModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "gte" + + def __init__( + self, + vocab_size=30528, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.0, + max_position_embeddings=2048, + type_vocab_size=1, + initializer_range=0.02, + layer_norm_type='layer_norm', + layer_norm_eps=1e-12, + # pad_token_id=0, + position_embedding_type="rope", + rope_theta=10000.0, + rope_scaling=None, + classifier_dropout=None, + pack_qkv=True, + unpad_inputs=False, + use_memory_efficient_attention=False, + logn_attention_scale=False, + logn_attention_clip1=False, + **kwargs, + ): + super().__init__(**kwargs) + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + self.layer_norm_type = layer_norm_type + self.layer_norm_eps = layer_norm_eps + self.position_embedding_type = position_embedding_type + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.classifier_dropout = classifier_dropout + + self.pack_qkv = pack_qkv + self.unpad_inputs = unpad_inputs + self.use_memory_efficient_attention = use_memory_efficient_attention + self.logn_attention_scale = logn_attention_scale + self.logn_attention_clip1 = logn_attention_clip1 \ No newline at end of file diff --git a/eval/Information-Retrieval_evaluation_Snowflake/snowflake-arctic-embed-m-v2.0_predictions_cosine.jsonl b/eval/Information-Retrieval_evaluation_Snowflake/snowflake-arctic-embed-m-v2.0_predictions_cosine.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a5daa6328a30a6598fbcba2b530287d9d9603804 --- /dev/null +++ b/eval/Information-Retrieval_evaluation_Snowflake/snowflake-arctic-embed-m-v2.0_predictions_cosine.jsonl @@ -0,0 +1,69 @@ +{"query_id": "63425", "query": "Userin kann Personal nicht \u00fcbertragen.", "results": [{"corpus_id": "63425", "score": 0.9999743103981018}, {"corpus_id": "67669", "score": 0.9112486839294434}, {"corpus_id": "49197", 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(c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + ModelOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +try: + import xformers.ops as xops +except ImportError as e: + xops = None + +from .configuration_hf_alibaba_nlp_gte import GteConfig + + +logger = logging.get_logger(__name__) + + +# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py +# Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py +class IndexFirstAxis(torch.autograd.Function): + @staticmethod + def forward(ctx, input, indices): + ctx.save_for_backward(indices) + assert input.ndim >= 2 + ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:] + second_dim = other_shape.numel() + # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing. + # return input[indices] + # return torch.gather( + # rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim) + # ).reshape(-1, *other_shape) + return torch.gather( + input.view(ctx.first_axis_dim, second_dim), + 0, + indices.unsqueeze(-1).expand(indices.size(0), second_dim) + ).reshape(-1, *other_shape) + + @staticmethod + def backward(ctx, grad_output): + (indices,) = ctx.saved_tensors + assert grad_output.ndim >= 2 + other_shape = grad_output.shape[1:] + # grad_output = rearrange(grad_output, "b ... -> b (...)") + grad_output = grad_output.view(grad_output.size(0), other_shape.numel()) + grad_input = torch.zeros( + [ctx.first_axis_dim, grad_output.shape[1]], + device=grad_output.device, + dtype=grad_output.dtype, + ) + # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing. + # grad_input[indices] = grad_output + # grad_input.scatter_(0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output) + grad_input.scatter_( + 0, indices.unsqueeze(-1).expand(indices.size(0), grad_output.size(1)), grad_output + ) + return grad_input.reshape(ctx.first_axis_dim, *other_shape), None + + +index_first_axis = IndexFirstAxis.apply + + +def unpad_input(hidden_states, attention_mask=None, indices=None): + """ + Arguments: + hidden_states: (batch, seqlen, ...) + attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid. + indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence. + Return: + hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask. + """ + if indices is None: + assert attention_mask is not None + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + + # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the + # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim + # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to + # index with integer indices. Moreover, torch's index is a bit slower than it needs to be, + # so we write custom forward and backward to make it a bit faster. + hidden_states = hidden_states.view(-1, *hidden_states.shape[2:]) + return index_first_axis(hidden_states, indices) + + +class IndexPutFirstAxis(torch.autograd.Function): + @staticmethod + def forward( + ctx, + values: torch.Tensor, + indices: torch.Tensor, + first_axis_dim + ) -> torch.Tensor: + ctx.save_for_backward(indices) + assert indices.ndim == 1 + assert values.ndim >= 2 + output = torch.zeros( + first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype + ) + output[indices] = values + return output + + @staticmethod + def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]: + indices, = ctx.saved_tensors + grad_values = grad_output[indices] + return grad_values, None, None + + +index_put_first_axis = IndexPutFirstAxis.apply + + +def pad_input(inputs: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor: + """Add padding to sequences. + + Arguments: + inputs: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask. + indices: (total_nnz), `indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()` + batch: int batch_size + seqlen: int max sequence length + + Returns: + inputs: (batch, seqlen, ...) + """ + output = index_put_first_axis(inputs, indices, batch * seqlen) + return output.view(batch, seqlen, *inputs.shape[1:]) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos, sin = cos.to(q.dtype), sin.to(q.dtype) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class RotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=512, base=10000.0, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:seq_len, ...].to(dtype=x.dtype), + ) + + +class NTKScalingRotaryEmbedding(RotaryEmbedding): + """RotaryEmbedding extended with fixed and mixed NTK scaling. https://kexue.fm/archives/9706 """ + + def __init__(self, dim, max_position_embeddings=512, base=10000, device=None, scaling_factor=1.0, mixed_b=None): + self.scaling_factor = scaling_factor + self.mixed_b = mixed_b + super().__init__(dim, max_position_embeddings, base, device) + max_position_embeddings = max_position_embeddings * self.scaling_factor + self._set_cos_sin_cache(max_position_embeddings, self.inv_freq.device, torch.get_default_dtype()) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * (self.scaling_factor if self.mixed_b is None else 1) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + + if self.mixed_b is None: + inv_freq = inv_freq / self.scaling_factor ** (2 / self.dim) # (6) + else: + a = torch.tensor(self.scaling_factor).log() / (self.dim / 2) ** self.mixed_b # (13) + lambda_1_m = (a * torch.arange(1, self.dim // 2 + 1).float().to(device) ** self.mixed_b).exp() # (12) + inv_freq = inv_freq / lambda_1_m # (10) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +class RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +LAYER_NORM = { + 'layer_norm': nn.LayerNorm, + 'rms_norm': RMSNorm +} + + +class GteEmbeddings(nn.Module): + """ + Embedding and Unpadding. + """ + + def __init__(self, config: GteConfig): + super().__init__() + self.padding_idx = config.pad_token_id + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=self.padding_idx + ) + + self.position_embedding_type = config.position_embedding_type + if self.position_embedding_type == 'absolute': + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + elif self.position_embedding_type == 'rope': + self._init_rope(config) + else: + raise ValueError + + self.type_vocab_size = config.type_vocab_size + if self.type_vocab_size > 0: + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids is contiguous in memory and excluded when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings), persistent=False + ) + + def _init_rope(self, config): + kwargs = dict( + dim=int(config.hidden_size / config.num_attention_heads), + max_position_embeddings=config.max_position_embeddings, + base=config.rope_theta + ) + if config.rope_scaling is None: + self.rotary_emb = RotaryEmbedding(**kwargs) + else: + kwargs.update(scaling_factor=config.rope_scaling["factor"]) + scaling_type = config.rope_scaling["type"] + if scaling_type == 'ntk': + kwargs.update(mixed_b=config.rope_scaling.get('mixed_b', None)) + self.rotary_emb = NTKScalingRotaryEmbedding(**kwargs) + # elif scaling_type == "linear": + # self.rotary_emb = LinearScalingRotaryEmbedding(**kwargs) + # elif scaling_type == "dynamic": + # self.rotary_emb = DynamicNTKScalingRotaryEmbedding(**kwargs) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def forward( + self, + unpad_inputs: bool, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + length: Optional[List[int]] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[Tuple], Optional[List[int]]]: + """ + """ + if inputs_embeds is None: + device, input_shape = input_ids.device, input_ids.shape + else: + device, input_shape = inputs_embeds.device, inputs_embeds.shape[:2] + batch_size, seq_length = input_shape + + # Set attention_mask if it's None + if attention_mask is None: + attention_mask = torch.ones(input_shape, device=device) + if length is not None: + for i, l in enumerate(length): + attention_mask[i, l:] = 0 + + # Set attention_mask_bool for unpadding + if unpad_inputs: + attention_mask_bool = attention_mask.bool() + if length is None: + length = attention_mask.sum(-1).tolist() + + # Get word embeddings + if inputs_embeds is None: + if unpad_inputs: + input_ids = input_ids[attention_mask_bool].unsqueeze(0) + inputs_embeds = self.word_embeddings(input_ids) + else: + if unpad_inputs: + inputs_embeds = inputs_embeds[attention_mask_bool].unsqueeze(0) + embeddings = inputs_embeds + + # Set and unpad position_ids + if position_ids is None: + if seq_length > self.position_ids.size(0): + self.register_buffer( + "position_ids", torch.arange(seq_length, device=embeddings.device), persistent=False + ) + if unpad_inputs: + # [1, cumsum_seq_len] + position_ids = torch.cat([self.position_ids[:l] for l in length]).unsqueeze(0) + else: + # [bs, seq_len] + position_ids = self.position_ids[:seq_length].expand(batch_size, -1) + elif unpad_inputs: + position_ids = position_ids[attention_mask_bool].unsqueeze(0) # [1, cumsum_seq_len] + + # Compute rotary embedding + if self.position_embedding_type == 'rope': + rope_cos, rope_sin = self.rotary_emb(inputs_embeds, seq_len=seq_length) + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + rope_sin = rope_sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + rope_embeds = rope_cos, rope_sin + else: + rope_embeds = None + + if self.type_vocab_size > 0: + if token_type_ids is None: + token_type_ids = position_ids.mul(0) + else: + if self.type_vocab_size < 2: + token_type_ids.mul_(0) + if unpad_inputs: + token_type_ids = token_type_ids[attention_mask_bool].unsqueeze(0) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = embeddings + token_type_embeddings + + # BERT position + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + + return embeddings, attention_mask, rope_embeds, length + + +class GteAttention(nn.Module): + def __init__(self, config: GteConfig, pack_qkv=None, use_memory_efficient_attention=None): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + if pack_qkv is None: + pack_qkv = config.pack_qkv + self.pack_qkv = pack_qkv + + if self.pack_qkv: + self.qkv_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=True) + else: + self.q_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + self.k_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + self.v_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True) + + if use_memory_efficient_attention is None: + use_memory_efficient_attention = self.config.use_memory_efficient_attention + self.use_memory_efficient_attention = use_memory_efficient_attention + self.memory_efficient_attention = None if xops is None else xops.memory_efficient_attention + if self.use_memory_efficient_attention: + assert self.memory_efficient_attention is not None, 'please install xformers' + + def forward( + self, + hidden_states: torch.Tensor, + attention_bias: torch.FloatTensor, + rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, + padding_inputs: Optional[Tuple] = None, # indices, batch, seqlen + attention_scale: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + qkv_inputs: Optional[Tuple] = None, # For RetroMAE + ) -> Tuple[torch.Tensor, ...]: + shape_hd = (self.num_attention_heads, self.attention_head_size) + # qkv + if self.pack_qkv and qkv_inputs is None: + qkv_pack = self.qkv_proj(hidden_states).split(self.all_head_size, dim=-1) + else: + if qkv_inputs is None: + qkv_inputs = (hidden_states, hidden_states, hidden_states) + qkv_pack = [ + getattr(self, n + '_proj')(s) for s, n in zip(qkv_inputs, 'qkv') + ] + query_states, key_states, value_states = [t.view(t.shape[:-1] + shape_hd) for t in qkv_pack] + + if self.config.position_embedding_type == 'rope': + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, *rope_embeds) + + dtype = query_states.dtype + + if self.config.logn_attention_scale and attention_scale is not None: + # https://kexue.fm/archives/8823 + query_states = query_states * attention_scale.to(dtype) + + if padding_inputs is not None: + query_states = pad_input(query_states.squeeze(), *padding_inputs) + key_states = pad_input(key_states.squeeze(), *padding_inputs) + value_states = pad_input(value_states.squeeze(), *padding_inputs) + + if self.use_memory_efficient_attention: + assert self.memory_efficient_attention is not None, "xformers is not loaded" + assert output_attentions is False, "memory_efficient_attention do not output attentions" + assert head_mask is None, "Not support yet" + attention_probs = None + if torch.is_tensor(attention_bias): + attention_bias = attention_bias.to(dtype) + context_layer = self.memory_efficient_attention( + query_states, + key_states, + value_states, + attn_bias=attention_bias, + p=self.dropout.p + ) + else: + if output_attentions and isinstance(self, GteSdpaAttention): + raise RuntimeError("SDPA do not output attentions") + context_layer, attention_probs = self._attention( + query_states, key_states, value_states, attention_bias, head_mask + ) + + if padding_inputs is not None: + context_layer = unpad_input(context_layer, indices=padding_inputs[0]) + + gte_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(gte_context_layer_shape) + + # output proj + attn_output = self.o_proj(context_layer) + + # add attentions if we output them + outputs = (attn_output, attention_probs) if output_attentions else (attn_output,) + return outputs + + def _attention(self, query_states, key_states, value_states, attention_bias, head_mask): + """ + Args: + q/k/v: (B, L, n_head, head_dim), + Returns: + attn_output: (B L, n_head, head_dim) + """ + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_bias is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_bias + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + if self.dropout.p > 0: + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_states) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + return context_layer, attention_probs + + +class GteSdpaAttention(GteAttention): + """ + Gte attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `GteAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + def __init__(self, config: GteConfig, **kwargs): + super().__init__(config, **kwargs) + # torch.backends.cuda.enable_mem_efficient_sdp(False) + # logger.warning( + # "Disable memory efficient attention kernel for `GteSdpaAttention`, you can set " + # "`use_memory_efficient_attention=True` if it expected to use." + # ) + + def _attention(self, query_states, key_states, value_states, attention_bias, head_mask): + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states.transpose(1, 2), + key_states.transpose(1, 2), + value_states.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout.p if self.training else 0.0, + ) + attn_output = attn_output.permute(0, 2, 1, 3).contiguous() + return attn_output, None + + +GTE_ATTENTION_CLASSES = { + "eager": GteAttention, + # "flash_attention_2": , # TODO + "sdpa": GteSdpaAttention, +} + + +class GteGatedMLP(nn.Module): + """ + GLU Variants Improve Transformer. + """ + + def __init__(self, config: GteConfig): + super().__init__() + self.intermediate_size = config.intermediate_size + self.up_gate_proj = nn.Linear(config.hidden_size, self.intermediate_size * 2, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=True) + self.act_fn = ACT2FN[config.hidden_act] + if config.hidden_dropout_prob > 0: + self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob) + else: + self.hidden_dropout = None + + def forward(self, hidden_states): + up_gate = self.up_gate_proj(hidden_states) + up_states, gate = torch.split(up_gate, self.intermediate_size, dim=-1) + gate = self.act_fn(gate) + gated_states = gate * up_states + if self.hidden_dropout is not None: + gated_states = self.hidden_dropout(gated_states) + down_states = self.down_proj(gated_states) + return down_states + + +class GteLayer(nn.Module): + def __init__( + self, + config: GteConfig, + pack_qkv=None, + use_memory_efficient_attention=None, + attn_implementation=None + ): + super().__init__() + if attn_implementation is None: + attn_implementation = config._attn_implementation + if use_memory_efficient_attention is None: + use_memory_efficient_attention = config.use_memory_efficient_attention + if use_memory_efficient_attention: + if attn_implementation != 'eager': + logger.warning_once(f"Override {attn_implementation=} to 'eager' as {use_memory_efficient_attention=}") + attn_implementation = 'eager' # Since it will be SDPA by default for torch>=2.1.1 + self.attention = GTE_ATTENTION_CLASSES[attn_implementation]( + config, pack_qkv=pack_qkv, use_memory_efficient_attention=use_memory_efficient_attention + ) + self.mlp = GteGatedMLP(config) + + ln_class = LAYER_NORM[config.layer_norm_type] + self.attn_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps) + self.mlp_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps) + + if config.hidden_dropout_prob > 0: + self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob) + else: + self.hidden_dropout = None + + def forward( + self, + hidden_states: torch.Tensor, + attention_bias: torch.FloatTensor, + rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, + padding_inputs: Optional[Tuple] = None, # indices, batch, seqlen + attention_scale: Optional[torch.FloatTensor] = None, + subset_indices: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + qkv_inputs: Optional[Tuple] = None, # For RetroMAE + ) -> Tuple[torch.Tensor, ...]: + # Multi head self attention + residual = hidden_states if qkv_inputs is None else qkv_inputs[0] + attention_outputs = self.attention( + hidden_states, + attention_bias, + rope_embeds, + padding_inputs, + attention_scale, + head_mask, + output_attentions=output_attentions, + qkv_inputs=qkv_inputs, + ) + hidden_states = attention_outputs[0] + if self.hidden_dropout is not None: + hidden_states = self.hidden_dropout(hidden_states) + hidden_states = residual + hidden_states + + # In pretraining, after the attention of last layer, we only need the masked tokens. + if subset_indices is not None: + hidden_states = hidden_states[subset_indices] + + hidden_states = self.attn_ln(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + if self.hidden_dropout is not None: + hidden_states = self.hidden_dropout(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.mlp_ln(hidden_states) + + # add self attentions if we output attention weights + outputs = (hidden_states,) + attention_outputs[1:] + return outputs + + +class GteEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([GteLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_bias: Optional[torch.FloatTensor] = None, + rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, + padding_inputs: Optional[Tuple] = None, # indices, batch, seqlen + attention_scale: Optional[torch.FloatTensor] = None, + subset_indices: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + ) -> Union[Tuple[torch.Tensor], BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if i >= len(self.layer) - 1: + layer_subset_indices = subset_indices + else: + layer_subset_indices = None + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_bias, + rope_embeds, + padding_inputs, + attention_scale, + layer_subset_indices, + layer_head_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_bias, + rope_embeds, + padding_inputs, + attention_scale, + layer_subset_indices, + layer_head_mask, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + all_hidden_states, + all_self_attentions, + ] + if v is not None + ) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Gte +class GtePooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class GtePreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = GteConfig + base_model_prefix = "gte" + supports_gradient_checkpointing = True + _supports_sdpa = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +class GteModel(GtePreTrainedModel): + """ + The bare Gte Model transformer outputting raw hidden-states without any specific head on top. + """ + + def __init__(self, config: GteConfig, add_pooling_layer=False): + super().__init__(config) + self.config = config + + self.embeddings = GteEmbeddings(config) + self.encoder = GteEncoder(config) + + self.pooler = GtePooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + length: Optional[List[int]] = None, + subset_indices: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + unpad_inputs: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]: + r""" + length (`list` of length `batch_size`, *optional*): + If is `None`, return padded `last_hidden_state`. + subset_indices (): + pass + unpad_inputs (`bool`, *optional*): + pass + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + unpad_inputs = unpad_inputs if unpad_inputs is not None else self.config.unpad_inputs + output_padded = length is None + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + # TODO: not used + # # Prepare head mask if needed + # # 1.0 in head_mask indicate we keep the head + # # attention_probs has shape bsz x n_heads x N x N + # # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + # head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + # Get embeddings, may unpad them + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + unpad_inputs, + input_ids=input_ids, + attention_mask=attention_mask, + length=length, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds + ) + + batch_size, seq_length = input_shape + if unpad_inputs and self.config.use_memory_efficient_attention: + attention_bias = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(length) + else: + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + attention_bias = self.get_extended_attention_mask(attention_mask, input_shape) + if self.config.use_memory_efficient_attention: + # Invalid shape for attention bias: torch.Size([48, 1, 1, 512]) (expected (48, 12, 512, 512)) + attention_bias = attention_bias.expand(-1, self.config.num_attention_heads, seq_length, -1) + + padding_inputs = None + if unpad_inputs and (output_padded or not self.config.use_memory_efficient_attention): + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + if not self.config.use_memory_efficient_attention: + padding_inputs = (indices, *input_shape) + + attention_scale = None + if self.config.logn_attention_scale: + logger.warning_once("TODO: logn_attention_scale") + # # attention scale log_512(input_len) + # attention_scale = attention_mask.sum(1).log() / torch.tensor(self.config.max_position_embeddings).log() + # # inference-time logn scale need clip 1 + # if self.config.logn_attention_clip1: + # attention_scale.clip_(1) + # attention_scale = attention_scale[:, None, None, None] + # else: + # attention_scale = None + + encoder_outputs = self.encoder( + embedding_output, + attention_bias=attention_bias, + rope_embeds=rope_embeds, + padding_inputs=padding_inputs, + attention_scale=attention_scale, + subset_indices=subset_indices, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + if unpad_inputs and output_padded: + sequence_output = pad_input( + sequence_output.squeeze(), indices, batch_size, seq_length + ) + + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) diff --git a/modules.json b/modules.json new file mode 100644 index 0000000000000000000000000000000000000000..c02d0ed3297a8a1a1c5e8ba6488447ede321dc51 --- /dev/null +++ b/modules.json @@ -0,0 +1,20 @@ +[ + { + "idx": 0, + "name": "0", + "path": "", + "type": "sentence_transformers.models.Transformer" + }, + { + "idx": 1, + "name": "1", + "path": "1_Pooling", + "type": "sentence_transformers.models.Pooling" + }, + { + "idx": 2, + "name": "2", + "path": "2_Normalize", + "type": "sentence_transformers.models.Normalize" + } +] \ No newline at end of file diff --git a/runs/Sep01_15-09-38_DESKTOP-JKAL6UH/events.out.tfevents.1756732179.DESKTOP-JKAL6UH b/runs/Sep01_15-09-38_DESKTOP-JKAL6UH/events.out.tfevents.1756732179.DESKTOP-JKAL6UH new file mode 100644 index 0000000000000000000000000000000000000000..d3c16628a6c16eee9dd41b79f18ddd5df4e01f24 --- /dev/null +++ b/runs/Sep01_15-09-38_DESKTOP-JKAL6UH/events.out.tfevents.1756732179.DESKTOP-JKAL6UH @@ 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