Add new CrossEncoder model

README.md

@@ -1,24 +1,49 @@
 ---
 tags:
 - sentence-transformers
-- sentence-similarity
-- feature-extraction
-pipeline_tag: sentence-similarity
+- cross-encoder
+- generated_from_trainer
+- dataset_size:22258
+- loss:FitMixinLoss
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+pipeline_tag: text-ranking
 library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: cross rerank dev mixed neg
+      type: cross-rerank-dev-mixed-neg
+    metrics:
+    - type: map
+      value: 0.4873053613053613
+      name: Map
+    - type: mrr@10
+      value: 0.48394871794871797
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5970778430138177
+      name: Ndcg@10
 ---
 
-# SentenceTransformer
+# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
 
 ## Model Details
 
 ### Model Description
-- **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision ce0834f22110de6d9222af7a7a03628121708969 -->
 - **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 384 dimensions
-- **Similarity Function:** Cosine Similarity
+- **Number of Output Labels:** 1 label
 <!-- - **Training Dataset:** Unknown -->
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
@@ -26,17 +51,9 @@ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps
 ### Model Sources
 
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
 - **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': 512, '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})
-)
-```
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
 
 ## Usage
 
@@ -50,24 +67,34 @@ pip install -U sentence-transformers
 
 Then you can load this model and run inference.
 ```python
-from sentence_transformers import SentenceTransformer
+from sentence_transformers import CrossEncoder
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("CharlesPing/finetuned-cross-encoder-l6-v2")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
+model = CrossEncoder("CharlesPing/finetuned-cross-encoder-l6-v2")
+# Get scores for pairs of texts
+pairs = [
+    ['‘Getting hung up on the exact nature of the records is interesting, and there’s lots of technical work that can be done there, but the main take-home response there is that the trends we’ve been seeing since the 1970s are continuing and have not paused in any way,’ he said.”', 'Rosenzweig also criticized the "waffling—encouraged by the NPOV policy—[which] means that it is hard to discern any overall interpretive stance in Wikipedia history".'],
+    ['After the 9/11 terrorist attacks grounded commercial air traffic, "there was a temperature drop while the airplanes weren\'t flying, for the week afterwards."', 'Play media At 9:42\xa0a.m., the Federal Aviation Administration (FAA) grounded all civilian aircraft within the continental U.S., and civilian aircraft already in flight were told to land immediately.'],
+    ['But the central message of the IPCC AR4, is confirmed by the peer reviewed literature.', 'Scientific consensus is normally achieved through communication at conferences, publication in the scientific literature, replication (reproducible results by others), and peer review.'],
+    ['"Many people think the science of climate change is settled.', 'During his administration, the bridge from Filadelfia and Liberia was constructed, as was the Old National Theater.'],
+    ['“Even if you could calculate some sort of meaningful global temperature statistic, the figure would be unimportant.', 'Quantitative information or data is based on quantities obtained using a quantifiable measurement process.'],
 ]
-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]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    '‘Getting hung up on the exact nature of the records is interesting, and there’s lots of technical work that can be done there, but the main take-home response there is that the trends we’ve been seeing since the 1970s are continuing and have not paused in any way,’ he said.”',
+    [
+        'Rosenzweig also criticized the "waffling—encouraged by the NPOV policy—[which] means that it is hard to discern any overall interpretive stance in Wikipedia history".',
+        'Play media At 9:42\xa0a.m., the Federal Aviation Administration (FAA) grounded all civilian aircraft within the continental U.S., and civilian aircraft already in flight were told to land immediately.',
+        'Scientific consensus is normally achieved through communication at conferences, publication in the scientific literature, replication (reproducible results by others), and peer review.',
+        'During his administration, the bridge from Filadelfia and Liberia was constructed, as was the Old National Theater.',
+        'Quantitative information or data is based on quantities obtained using a quantifiable measurement process.',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
 ```
 
 <!--
@@ -94,6 +121,26 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Reranking
+
+* Dataset: `cross-rerank-dev-mixed-neg`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10
+  }
+  ```
+
+| Metric      | Value      |
+|:------------|:-----------|
+| map         | 0.4873     |
+| mrr@10      | 0.4839     |
+| **ndcg@10** | **0.5971** |
+
 <!--
 ## Bias, Risks and Limitations
 
@@ -108,19 +155,193 @@ You can finetune this model on your own dataset.
 
 ## Training Details
 
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 22,258 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                                       | sentence_1                                                                                       | label                                                          |
+  |:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                           | string                                                                                           | float                                                          |
+  | details | <ul><li>min: 26 characters</li><li>mean: 121.91 characters</li><li>max: 319 characters</li></ul> | <ul><li>min: 36 characters</li><li>mean: 140.85 characters</li><li>max: 573 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.16</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                       | sentence_1                                                                                                                                                                                                         | label            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>‘Getting hung up on the exact nature of the records is interesting, and there’s lots of technical work that can be done there, but the main take-home response there is that the trends we’ve been seeing since the 1970s are continuing and have not paused in any way,’ he said.”</code> | <code>Rosenzweig also criticized the "waffling—encouraged by the NPOV policy—[which] means that it is hard to discern any overall interpretive stance in Wikipedia history".</code>                                | <code>1.0</code> |
+  | <code>After the 9/11 terrorist attacks grounded commercial air traffic, "there was a temperature drop while the airplanes weren't flying, for the week afterwards."</code>                                                                                                                       | <code>Play media At 9:42 a.m., the Federal Aviation Administration (FAA) grounded all civilian aircraft within the continental U.S., and civilian aircraft already in flight were told to land immediately.</code> | <code>1.0</code> |
+  | <code>But the central message of the IPCC AR4, is confirmed by the peer reviewed literature.</code>                                                                                                                                                                                              | <code>Scientific consensus is normally achieved through communication at conferences, publication in the scientific literature, replication (reproducible results by others), and peer review.</code>              | <code>1.0</code> |
+* Loss: [<code>FitMixinLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#fitmixinloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+
+#### 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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}
+- `tp_size`: 0
+- `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`: None
+- `hub_always_push`: False
+- `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
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | cross-rerank-dev-mixed-neg_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------------------:|
+| 0.3592 | 500  | 0.4259        | 0.5154                             |
+| 0.7184 | 1000 | 0.3346        | 0.5497                             |
+| 1.0    | 1392 | -             | 0.5640                             |
+| 1.0776 | 1500 | 0.3171        | 0.5660                             |
+| 1.4368 | 2000 | 0.2826        | 0.5669                             |
+| 1.7960 | 2500 | 0.281         | 0.5802                             |
+| 2.0    | 2784 | -             | 0.5834                             |
+| 2.1552 | 3000 | 0.2553        | 0.5842                             |
+| 2.5144 | 3500 | 0.2326        | 0.5961                             |
+| 2.8736 | 4000 | 0.2408        | 0.5971                             |
+
+
 ### Framework Versions
 - Python: 3.11.12
 - Sentence Transformers: 4.1.0
 - Transformers: 4.51.3
 - PyTorch: 2.6.0+cu124
 - Accelerate: 1.6.0
-- Datasets: 2.14.4
+- Datasets: 3.5.1
 - Tokenizers: 0.21.1
 
 ## 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",
+}
+```
+
 <!--
 ## Glossary
 

config.json

@@ -1,6 +1,6 @@
 {
   "architectures": [
-    "BertModel"
+    "BertForSequenceClassification"
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a610f0f065d2463387fb99e089e7317101263dc261e5cc4fda16fc8cd9503cc7
-size 90864192
+oid sha256:bafddd7c2f5c1337838bd6699c67487afdd865a9e71bca20d6754a95520b0614
+size 90866412