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checkpoint-13500/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-13500/README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:7747936
+- loss:CoSENTLoss
+widget:
+- source_sentence: mango cake cream cake sponge cake gateau mango gateau cream gateau
+    mango sponge cake cream sponge cake mango cream cake mango cream sponge cake mango
+    flavored sponge cake layers cream filling decorated with fresh mango slices topped
+    with whipped cream serves 10 people mango cream cake sponge cake gateau mango
+    gateau with cream filling whipped cream mango cake mango cream sponge cake for
+    10 people
+  sentences:
+  - vegan dessert
+  - oxidized ring
+  - cola lip gloss
+- source_sentence: double breasted blouse
+  sentences:
+  - brushed jersey sweatshirt
+  - comfort facial tissues
+  - round neck sweatshirt
+- source_sentence: casual shirt
+  sentences:
+  - adjustable string top
+  - foldable spare backpack
+  - spring blossom scent shower gel
+- source_sentence: sweet chilli mozzarella stick
+  sentences:
+  - fragrance free facial cream
+  - outdoor basket
+  - cobb dressing salad
+- source_sentence: appetizer onion ring
+  sentences:
+  - high quality sports bra
+  - swimmer burkini
+  - nuttella pizza
+---
+
+# all-MiniLM-L6-v10-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'appetizer onion ring',
+    'nuttella pizza',
+    'high quality sports bra',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `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`: False
+- `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
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0017 | 100   | 13.3171       |
+| 0.0033 | 200   | 12.9799       |
+| 0.0050 | 300   | 12.5133       |
+| 0.0066 | 400   | 11.9388       |
+| 0.0083 | 500   | 11.0616       |
+| 0.0099 | 600   | 10.2712       |
+| 0.0116 | 700   | 9.5253        |
+| 0.0132 | 800   | 8.7706        |
+| 0.0149 | 900   | 8.4333        |
+| 0.0165 | 1000  | 8.0902        |
+| 0.0182 | 1100  | 7.8862        |
+| 0.0198 | 1200  | 7.7362        |
+| 0.0215 | 1300  | 7.6007        |
+| 0.0231 | 1400  | 7.5304        |
+| 0.0248 | 1500  | 7.4249        |
+| 0.0264 | 1600  | 7.3035        |
+| 0.0281 | 1700  | 7.2026        |
+| 0.0297 | 1800  | 7.1572        |
+| 0.0314 | 1900  | 7.0523        |
+| 0.0330 | 2000  | 7.1158        |
+| 0.0347 | 2100  | 6.9856        |
+| 0.0363 | 2200  | 7.0865        |
+| 0.0380 | 2300  | 6.9496        |
+| 0.0396 | 2400  | 6.9294        |
+| 0.0413 | 2500  | 6.8825        |
+| 0.0430 | 2600  | 6.8218        |
+| 0.0446 | 2700  | 6.8416        |
+| 0.0463 | 2800  | 6.7184        |
+| 0.0479 | 2900  | 6.9183        |
+| 0.0496 | 3000  | 6.7166        |
+| 0.0512 | 3100  | 6.6821        |
+| 0.0529 | 3200  | 6.6074        |
+| 0.0545 | 3300  | 6.6141        |
+| 0.0562 | 3400  | 6.5374        |
+| 0.0578 | 3500  | 6.4776        |
+| 0.0595 | 3600  | 6.5701        |
+| 0.0611 | 3700  | 6.5026        |
+| 0.0628 | 3800  | 6.6502        |
+| 0.0644 | 3900  | 6.5023        |
+| 0.0661 | 4000  | 6.5526        |
+| 0.0677 | 4100  | 6.6594        |
+| 0.0694 | 4200  | 6.3643        |
+| 0.0710 | 4300  | 6.3783        |
+| 0.0727 | 4400  | 6.3222        |
+| 0.0743 | 4500  | 6.3401        |
+| 0.0760 | 4600  | 6.4005        |
+| 0.0776 | 4700  | 6.3605        |
+| 0.0793 | 4800  | 6.348         |
+| 0.0810 | 4900  | 6.3406        |
+| 0.0826 | 5000  | 6.4156        |
+| 0.0843 | 5100  | 6.3786        |
+| 0.0859 | 5200  | 6.376         |
+| 0.0876 | 5300  | 6.2363        |
+| 0.0892 | 5400  | 6.2185        |
+| 0.0909 | 5500  | 6.2554        |
+| 0.0925 | 5600  | 6.2177        |
+| 0.0942 | 5700  | 6.3924        |
+| 0.0958 | 5800  | 6.2897        |
+| 0.0975 | 5900  | 6.272         |
+| 0.0991 | 6000  | 6.0247        |
+| 0.1008 | 6100  | 6.194         |
+| 0.1024 | 6200  | 6.2757        |
+| 0.1041 | 6300  | 6.2408        |
+| 0.1057 | 6400  | 6.253         |
+| 0.1074 | 6500  | 6.0605        |
+| 0.1090 | 6600  | 6.0672        |
+| 0.1107 | 6700  | 6.0414        |
+| 0.1123 | 6800  | 6.0823        |
+| 0.1140 | 6900  | 6.1962        |
+| 0.1156 | 7000  | 6.0868        |
+| 0.1173 | 7100  | 6.0795        |
+| 0.1189 | 7200  | 5.9656        |
+| 0.1206 | 7300  | 5.9785        |
+| 0.1223 | 7400  | 6.0722        |
+| 0.1239 | 7500  | 5.9443        |
+| 0.1256 | 7600  | 5.8786        |
+| 0.1272 | 7700  | 5.8007        |
+| 0.1289 | 7800  | 5.9206        |
+| 0.1305 | 7900  | 5.918         |
+| 0.1322 | 8000  | 5.9443        |
+| 0.1338 | 8100  | 5.8764        |
+| 0.1355 | 8200  | 5.867         |
+| 0.1371 | 8300  | 5.8087        |
+| 0.1388 | 8400  | 5.9884        |
+| 0.1404 | 8500  | 5.8741        |
+| 0.1421 | 8600  | 5.9699        |
+| 0.1437 | 8700  | 5.8671        |
+| 0.1454 | 8800  | 5.8278        |
+| 0.1470 | 8900  | 5.8892        |
+| 0.1487 | 9000  | 5.7437        |
+| 0.1503 | 9100  | 5.8069        |
+| 0.1520 | 9200  | 6.0235        |
+| 0.1536 | 9300  | 5.7214        |
+| 0.1553 | 9400  | 5.7893        |
+| 0.1569 | 9500  | 5.7406        |
+| 0.1586 | 9600  | 5.8035        |
+| 0.1602 | 9700  | 5.7965        |
+| 0.1619 | 9800  | 5.638         |
+| 0.1636 | 9900  | 5.8263        |
+| 0.1652 | 10000 | 5.7995        |
+| 0.1669 | 10100 | 5.5805        |
+| 0.1685 | 10200 | 5.632         |
+| 0.1702 | 10300 | 5.6944        |
+| 0.1718 | 10400 | 5.5818        |
+| 0.1735 | 10500 | 5.8598        |
+| 0.1751 | 10600 | 5.7255        |
+| 0.1768 | 10700 | 5.7536        |
+| 0.1784 | 10800 | 5.6536        |
+| 0.1801 | 10900 | 5.6417        |
+| 0.1817 | 11000 | 5.6719        |
+| 0.1834 | 11100 | 5.566         |
+| 0.1850 | 11200 | 5.4893        |
+| 0.1867 | 11300 | 5.7412        |
+| 0.1883 | 11400 | 5.6838        |
+| 0.1900 | 11500 | 5.6272        |
+| 0.1916 | 11600 | 5.6538        |
+| 0.1933 | 11700 | 5.7176        |
+| 0.1949 | 11800 | 5.4923        |
+| 0.1966 | 11900 | 5.7643        |
+| 0.1982 | 12000 | 5.5674        |
+| 0.1999 | 12100 | 5.6896        |
+| 0.2015 | 12200 | 5.4385        |
+| 0.2032 | 12300 | 5.5851        |
+| 0.2049 | 12400 | 5.5132        |
+| 0.2065 | 12500 | 5.3329        |
+| 0.2082 | 12600 | 5.4218        |
+| 0.2098 | 12700 | 5.5171        |
+| 0.2115 | 12800 | 5.3414        |
+| 0.2131 | 12900 | 5.4921        |
+| 0.2148 | 13000 | 5.7687        |
+| 0.2164 | 13100 | 5.7119        |
+| 0.2181 | 13200 | 5.4975        |
+| 0.2197 | 13300 | 5.4514        |
+| 0.2214 | 13400 | 5.497         |
+| 0.2230 | 13500 | 5.558         |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-13500/config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-13500/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.1+cu118"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

checkpoint-13500/model.safetensors

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+size 90864192

checkpoint-13500/modules.json

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+[
+  {
+    "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"
+  }
+]

checkpoint-13500/optimizer.pt

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checkpoint-13500/rng_state.pth

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checkpoint-13500/scheduler.pt

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checkpoint-13500/sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

checkpoint-13500/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-13500/tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
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