Add new CrossEncoder model

README.md

@@ -0,0 +1,481 @@
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:942069
+- loss:BinaryCrossEntropyLoss
+base_model: microsoft/deberta-v3-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- accuracy
+- accuracy_threshold
+- f1
+- f1_threshold
+- precision
+- recall
+- average_precision
+model-index:
+- name: CrossEncoder based on microsoft/deberta-v3-base
+  results:
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: AllNLI norm dev
+      type: AllNLI-norm-dev
+    metrics:
+    - type: accuracy
+      value: 0.6807244238693595
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.4375791847705841
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.5465734265734266
+      name: F1
+    - type: f1_threshold
+      value: 0.00438243243843317
+      name: F1 Threshold
+    - type: precision
+      value: 0.40035514274006284
+      name: Precision
+    - type: recall
+      value: 0.8610458284371327
+      name: Recall
+    - type: average_precision
+      value: 0.49929639749742777
+      name: Average Precision
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: AllNLI test
+      type: AllNLI-test
+    metrics:
+    - type: accuracy
+      value: 0.6814204314204314
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.5599576234817505
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.5269568771714694
+      name: F1
+    - type: f1_threshold
+      value: 0.0010413693962618709
+      name: F1 Threshold
+    - type: precision
+      value: 0.3655438357718045
+      name: Precision
+    - type: recall
+      value: 0.9436392914653784
+      name: Recall
+    - type: average_precision
+      value: 0.48186391164637776
+      name: Average Precision
+---
+
+# CrossEncoder based on microsoft/deberta-v3-base
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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("tani-at-nola/reranker-deberta-v3-base-nli")
+# Get scores for pairs of texts
+pairs = [
+    ['The sisters are hugging goodbye while holding to go packages after just eating lunch.', 'Two women are embracing while holding to go packages.'],
+    ['Two woman are holding packages.', 'Two women are embracing while holding to go packages.'],
+    ['The men are fighting outside a deli.', 'Two women are embracing while holding to go packages.'],
+    ['Two kids in numbered jerseys wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'],
+    ['Two kids at a ballgame wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'The sisters are hugging goodbye while holding to go packages after just eating lunch.',
+    [
+        'Two women are embracing while holding to go packages.',
+        'Two women are embracing while holding to go packages.',
+        'Two women are embracing while holding to go packages.',
+        'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.',
+        'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.',
+    ]
+)
+# [{'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 Classification
+
+* Datasets: `AllNLI-norm-dev` and `AllNLI-test`
+* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+
+| Metric                | AllNLI-norm-dev | AllNLI-test |
+|:----------------------|:----------------|:------------|
+| accuracy              | 0.6807          | 0.6814      |
+| accuracy_threshold    | 0.4376          | 0.56        |
+| f1                    | 0.5466          | 0.527       |
+| f1_threshold          | 0.0044          | 0.001       |
+| precision             | 0.4004          | 0.3655      |
+| recall                | 0.861           | 0.9436      |
+| **average_precision** | **0.4993**      | **0.4819**  |
+
+<!--
+## 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: 942,069 training samples
+* Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | hypothesis                                                                                      | premise                                                                                         | label                                           |
+  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                          | int                                             |
+  | details | <ul><li>min: 11 characters</li><li>mean: 38.26 characters</li><li>max: 131 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 69.54 characters</li><li>max: 227 characters</li></ul> | <ul><li>0: ~66.60%</li><li>1: ~33.40%</li></ul> |
+* Samples:
+  | hypothesis                                                     | premise                                                             | label          |
+  |:---------------------------------------------------------------|:--------------------------------------------------------------------|:---------------|
+  | <code>A person is training his horse for a competition.</code> | <code>A person on a horse jumps over a broken down airplane.</code> | <code>0</code> |
+  | <code>A person is at a diner, ordering an omelette.</code>     | <code>A person on a horse jumps over a broken down airplane.</code> | <code>0</code> |
+  | <code>A person is outdoors, on a horse.</code>                 | <code>A person on a horse jumps over a broken down airplane.</code> | <code>1</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": null
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 19,657 evaluation samples
+* Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | hypothesis                                                                                      | premise                                                                                         | label                                           |
+  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                          | int                                             |
+  | details | <ul><li>min: 11 characters</li><li>mean: 37.66 characters</li><li>max: 116 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 75.01 characters</li><li>max: 229 characters</li></ul> | <ul><li>0: ~66.90%</li><li>1: ~33.10%</li></ul> |
+* Samples:
+  | hypothesis                                                                                         | premise                                                            | label          |
+  |:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------|
+  | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>Two women are embracing while holding to go packages.</code> | <code>0</code> |
+  | <code>Two woman are holding packages.</code>                                                       | <code>Two women are embracing while holding to go packages.</code> | <code>1</code> |
+  | <code>The men are fighting outside a deli.</code>                                                  | <code>Two women are embracing while holding to go packages.</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": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `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`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 1.0
+- `num_train_epochs`: 5
+- `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`: 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`: True
+- `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
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch      | Step     | Training Loss | Validation Loss | AllNLI-norm-dev_average_precision | AllNLI-test_average_precision |
+|:----------:|:--------:|:-------------:|:---------------:|:---------------------------------:|:-----------------------------:|
+| -1         | -1       | -             | -               | 0.3614                            | -                             |
+| 0.0068     | 100      | 0.7205        | -               | -                                 | -                             |
+| 0.0136     | 200      | 0.6972        | -               | -                                 | -                             |
+| 0.0204     | 300      | 0.6086        | -               | -                                 | -                             |
+| 0.0272     | 400      | 0.4855        | -               | -                                 | -                             |
+| 0.0340     | 500      | 0.3991        | -               | -                                 | -                             |
+| 0.0408     | 600      | 0.3409        | -               | -                                 | -                             |
+| 0.0476     | 700      | 0.2987        | -               | -                                 | -                             |
+| 0.0544     | 800      | 0.2841        | -               | -                                 | -                             |
+| 0.0611     | 900      | 0.2729        | -               | -                                 | -                             |
+| 0.0679     | 1000     | 0.2627        | -               | -                                 | -                             |
+| 0.0747     | 1100     | 0.2517        | -               | -                                 | -                             |
+| 0.0815     | 1200     | 0.2286        | -               | -                                 | -                             |
+| 0.0883     | 1300     | 0.2385        | -               | -                                 | -                             |
+| 0.0951     | 1400     | 0.2329        | -               | -                                 | -                             |
+| 0.1019     | 1500     | 0.2213        | 0.1959          | 0.4997                            | -                             |
+| 0.1087     | 1600     | 0.22          | -               | -                                 | -                             |
+| 0.1155     | 1700     | 0.2295        | -               | -                                 | -                             |
+| 0.1223     | 1800     | 0.2236        | -               | -                                 | -                             |
+| 0.1291     | 1900     | 0.2273        | -               | -                                 | -                             |
+| 0.1359     | 2000     | 0.2071        | -               | -                                 | -                             |
+| 0.1427     | 2100     | 0.2254        | -               | -                                 | -                             |
+| 0.1495     | 2200     | 0.2217        | -               | -                                 | -                             |
+| 0.1563     | 2300     | 0.2093        | -               | -                                 | -                             |
+| 0.1631     | 2400     | 0.2112        | -               | -                                 | -                             |
+| 0.1698     | 2500     | 0.2176        | -               | -                                 | -                             |
+| 0.1766     | 2600     | 0.2195        | -               | -                                 | -                             |
+| 0.1834     | 2700     | 0.2107        | -               | -                                 | -                             |
+| 0.1902     | 2800     | 0.2164        | -               | -                                 | -                             |
+| 0.1970     | 2900     | 0.213         | -               | -                                 | -                             |
+| **0.2038** | **3000** | **0.2055**    | **0.1726**      | **0.4789**                        | **-**                         |
+| 0.2106     | 3100     | 0.2039        | -               | -                                 | -                             |
+| 0.2174     | 3200     | 0.2157        | -               | -                                 | -                             |
+| 0.2242     | 3300     | 0.2155        | -               | -                                 | -                             |
+| 0.2310     | 3400     | 0.2017        | -               | -                                 | -                             |
+| 0.2378     | 3500     | 0.2068        | -               | -                                 | -                             |
+| 0.2446     | 3600     | 0.2111        | -               | -                                 | -                             |
+| 0.2514     | 3700     | 0.2062        | -               | -                                 | -                             |
+| 0.2582     | 3800     | 0.2062        | -               | -                                 | -                             |
+| 0.2650     | 3900     | 0.2217        | -               | -                                 | -                             |
+| 0.2718     | 4000     | 0.2012        | -               | -                                 | -                             |
+| 0.2786     | 4100     | 0.2127        | -               | -                                 | -                             |
+| 0.2853     | 4200     | 0.212         | -               | -                                 | -                             |
+| 0.2921     | 4300     | 0.2075        | -               | -                                 | -                             |
+| 0.2989     | 4400     | 0.2099        | -               | -                                 | -                             |
+| 0.3057     | 4500     | 0.2134        | 0.1644          | 0.4993                            | -                             |
+| -1         | -1       | -             | -               | -                                 | 0.4819                        |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.2
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.9.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.2
+
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

config.json

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+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.0.0"
+  },
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

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+size 2464616

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}