Add new CrossEncoder model

README.md

@@ -0,0 +1,530 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:2749365
+- loss:BinaryCrossEntropyLoss
+base_model: nreimers/MiniLM-L6-H384-uncased
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ModernBERT-base trained on GooAQ
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: gooaq dev
+      type: gooaq-dev
+    metrics:
+    - type: map
+      value: 0.5291
+      name: Map
+    - type: mrr@10
+      value: 0.5258
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5805
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.2939
+      name: Map
+    - type: mrr@10
+      value: 0.2772
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3678
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3242
+      name: Map
+    - type: mrr@10
+      value: 0.5253
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3345
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.2769
+      name: Map
+    - type: mrr@10
+      value: 0.2629
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3325
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.2984
+      name: Map
+    - type: mrr@10
+      value: 0.3552
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3449
+      name: Ndcg@10
+---
+
+# ModernBERT-base trained on GooAQ
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) 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:** Cross Encoder
+- **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("ayushexel/reranker-MiniLM-L6-H384-uncased-gooaq-bce-495000")
+# Get scores for pairs of texts
+pairs = [
+    ["in grey's anatomy how does izzie die?", 'After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.'],
+    ["in grey's anatomy how does izzie die?", "Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce."],
+    ["in grey's anatomy how does izzie die?", "The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy."],
+    ["in grey's anatomy how does izzie die?", "Richard Webber, Grey's Anatomy fans are nervous he'll die, though nothing is set in stone on the show yet. Warning: Spoilers for Season 16, Episode 19 of Grey's Anatomy follow."],
+    ["in grey's anatomy how does izzie die?", "Izzie eventually forgives him, and they begin dating again until Denny enters the picture. After Denny's death they begin dating yet again and following her recovery from cancer they get married, but it doesn't last."],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    "in grey's anatomy how does izzie die?",
+    [
+        'After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.',
+        "Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce.",
+        "The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy.",
+        "Richard Webber, Grey's Anatomy fans are nervous he'll die, though nothing is set in stone on the show yet. Warning: Spoilers for Season 16, Episode 19 of Grey's Anatomy follow.",
+        "Izzie eventually forgives him, and they begin dating again until Denny enters the picture. After Denny's death they begin dating yet again and following her recovery from cancer they get married, but it doesn't last.",
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Reranking
+
+* Dataset: `gooaq-dev`
+* 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,
+      "always_rerank_positives": false
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.5291 (+0.1486)     |
+| mrr@10      | 0.5258 (+0.1553)     |
+| **ndcg@10** | **0.5805 (+0.1477)** |
+
+#### Cross Encoder Reranking
+
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* 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,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.2939 (-0.1956)     | 0.3242 (+0.0632)     | 0.2769 (-0.1427)     |
+| mrr@10      | 0.2772 (-0.2003)     | 0.5253 (+0.0255)     | 0.2629 (-0.1638)     |
+| **ndcg@10** | **0.3678 (-0.1726)** | **0.3345 (+0.0095)** | **0.3325 (-0.1682)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.2984 (-0.0917)     |
+| mrr@10      | 0.3552 (-0.1128)     |
+| **ndcg@10** | **0.3449 (-0.1104)** |
+
+<!--
+## 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 Dataset
+
+#### Unnamed Dataset
+
+* Size: 2,749,365 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                                       | answer                                                                                           | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | int                                             |
+  | details | <ul><li>min: 19 characters</li><li>mean: 42.17 characters</li><li>max: 79 characters</li></ul> | <ul><li>min: 54 characters</li><li>mean: 246.01 characters</li><li>max: 399 characters</li></ul> | <ul><li>0: ~81.90%</li><li>1: ~18.10%</li></ul> |
+* Samples:
+  | question                                           | answer                                                                                                                                                                                                                                                                                                                                      | label          |
+  |:---------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>in grey's anatomy how does izzie die?</code> | <code>After speculation that Izzie would be killed off in the fifth season, the character was diagnosed with Stage 4 metastatic melanoma.</code>                                                                                                                                                                                            | <code>1</code> |
+  | <code>in grey's anatomy how does izzie die?</code> | <code>Izzie later admitted to George that she was in love with him, leaving him speechless. George later admitted he loved Izzie too, despite his strange reaction to her when she confessed her love to him. Their relationship was soon discovered by George's wife, Callie and the two got a divorce.</code>                             | <code>0</code> |
+  | <code>in grey's anatomy how does izzie die?</code> | <code>The episode in which Derek Shepherd (Patrick Dempsey) dies is one that most Grey's Anatomy fans will never forget. The fateful incident occurred in season 11, episode 21, and it was titled, “How To Save a Life.” The attending doctor who failed to save McDreamy's life recently appeared in an episode of Grey's Anatomy.</code> | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 12
+- `load_best_model_at_end`: 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`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 12
+- `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`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 12
+- `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}
+- `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
+- `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
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
+|:------:|:-----:|:-------------:|:-----------------:|:------------------------:|:-------------------------:|:-------------------:|:--------------------------:|
+| -1     | -1    | -             | 0.1141 (-0.3187)  | 0.0667 (-0.4737)         | 0.2984 (-0.0267)          | 0.0318 (-0.4689)    | 0.1323 (-0.3231)           |
+| 0.0001 | 1     | 1.2808        | -                 | -                        | -                         | -                   | -                          |
+| 0.0186 | 200   | 1.196         | -                 | -                        | -                         | -                   | -                          |
+| 0.0372 | 400   | 1.1939        | -                 | -                        | -                         | -                   | -                          |
+| 0.0559 | 600   | 1.1823        | -                 | -                        | -                         | -                   | -                          |
+| 0.0745 | 800   | 1.1506        | -                 | -                        | -                         | -                   | -                          |
+| 0.0931 | 1000  | 0.9972        | -                 | -                        | -                         | -                   | -                          |
+| 0.1117 | 1200  | 0.9336        | -                 | -                        | -                         | -                   | -                          |
+| 0.1304 | 1400  | 0.898         | -                 | -                        | -                         | -                   | -                          |
+| 0.1490 | 1600  | 0.8582        | -                 | -                        | -                         | -                   | -                          |
+| 0.1676 | 1800  | 0.8391        | -                 | -                        | -                         | -                   | -                          |
+| 0.1862 | 2000  | 0.8153        | -                 | -                        | -                         | -                   | -                          |
+| 0.2048 | 2200  | 0.7999        | -                 | -                        | -                         | -                   | -                          |
+| 0.2235 | 2400  | 0.7793        | -                 | -                        | -                         | -                   | -                          |
+| 0.2421 | 2600  | 0.7889        | -                 | -                        | -                         | -                   | -                          |
+| 0.2607 | 2800  | 0.7576        | -                 | -                        | -                         | -                   | -                          |
+| 0.2793 | 3000  | 0.7592        | -                 | -                        | -                         | -                   | -                          |
+| 0.2980 | 3200  | 0.7543        | -                 | -                        | -                         | -                   | -                          |
+| 0.3166 | 3400  | 0.7437        | -                 | -                        | -                         | -                   | -                          |
+| 0.3352 | 3600  | 0.7426        | -                 | -                        | -                         | -                   | -                          |
+| 0.3538 | 3800  | 0.7337        | -                 | -                        | -                         | -                   | -                          |
+| 0.3724 | 4000  | 0.7312        | -                 | -                        | -                         | -                   | -                          |
+| 0.3911 | 4200  | 0.7212        | -                 | -                        | -                         | -                   | -                          |
+| 0.4097 | 4400  | 0.7281        | -                 | -                        | -                         | -                   | -                          |
+| 0.4283 | 4600  | 0.7166        | -                 | -                        | -                         | -                   | -                          |
+| 0.4469 | 4800  | 0.7167        | -                 | -                        | -                         | -                   | -                          |
+| 0.4655 | 5000  | 0.7175        | -                 | -                        | -                         | -                   | -                          |
+| 0.4842 | 5200  | 0.7176        | -                 | -                        | -                         | -                   | -                          |
+| 0.5028 | 5400  | 0.7141        | -                 | -                        | -                         | -                   | -                          |
+| 0.5214 | 5600  | 0.6963        | -                 | -                        | -                         | -                   | -                          |
+| 0.5400 | 5800  | 0.6888        | -                 | -                        | -                         | -                   | -                          |
+| 0.5587 | 6000  | 0.6937        | -                 | -                        | -                         | -                   | -                          |
+| 0.5773 | 6200  | 0.7009        | -                 | -                        | -                         | -                   | -                          |
+| 0.5959 | 6400  | 0.6887        | -                 | -                        | -                         | -                   | -                          |
+| 0.6145 | 6600  | 0.6933        | -                 | -                        | -                         | -                   | -                          |
+| 0.6331 | 6800  | 0.692         | -                 | -                        | -                         | -                   | -                          |
+| 0.6518 | 7000  | 0.6874        | -                 | -                        | -                         | -                   | -                          |
+| 0.6704 | 7200  | 0.6792        | -                 | -                        | -                         | -                   | -                          |
+| 0.6890 | 7400  | 0.6772        | -                 | -                        | -                         | -                   | -                          |
+| 0.7076 | 7600  | 0.6804        | -                 | -                        | -                         | -                   | -                          |
+| 0.7263 | 7800  | 0.6728        | -                 | -                        | -                         | -                   | -                          |
+| 0.7449 | 8000  | 0.6703        | -                 | -                        | -                         | -                   | -                          |
+| 0.7635 | 8200  | 0.6844        | -                 | -                        | -                         | -                   | -                          |
+| 0.7821 | 8400  | 0.6663        | -                 | -                        | -                         | -                   | -                          |
+| 0.8007 | 8600  | 0.6775        | -                 | -                        | -                         | -                   | -                          |
+| 0.8194 | 8800  | 0.6647        | -                 | -                        | -                         | -                   | -                          |
+| 0.8380 | 9000  | 0.6818        | -                 | -                        | -                         | -                   | -                          |
+| 0.8566 | 9200  | 0.6724        | -                 | -                        | -                         | -                   | -                          |
+| 0.8752 | 9400  | 0.6748        | -                 | -                        | -                         | -                   | -                          |
+| 0.8939 | 9600  | 0.6567        | -                 | -                        | -                         | -                   | -                          |
+| 0.9125 | 9800  | 0.6682        | -                 | -                        | -                         | -                   | -                          |
+| 0.9311 | 10000 | 0.6747        | -                 | -                        | -                         | -                   | -                          |
+| 0.9497 | 10200 | 0.6618        | -                 | -                        | -                         | -                   | -                          |
+| 0.9683 | 10400 | 0.6625        | -                 | -                        | -                         | -                   | -                          |
+| 0.9870 | 10600 | 0.6629        | -                 | -                        | -                         | -                   | -                          |
+| -1     | -1    | -             | 0.5805 (+0.1477)  | 0.3678 (-0.1726)         | 0.3345 (+0.0095)          | 0.3325 (-0.1682)    | 0.3449 (-0.1104)           |
+
+
+### Framework Versions
+- Python: 3.11.0
+- Sentence Transformers: 4.0.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- 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
+
+*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.*
+-->

config.json

@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "4.0.1"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:312fddf1666d9cc05311f5bdae8ced187ae2f2c6373973d8f4e4ebf177e443fd
+size 90866412

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "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
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}