Finetuned model on SNLI

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
+}

README.md

@@ -0,0 +1,467 @@
+---
+base_model: sentence-transformers/all-MiniLM-L12-v2
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:100000
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: A boy wearing climbing gear climbs by a wooden pole.
+  sentences:
+  - A person wearing climbing gear climbs by a wooden pole.
+  - A man holds up a tent pole.
+  - A man plays an instrument.
+- source_sentence: Asian men saying hello to each other.
+  sentences:
+  - Asian men are about to attend a convention.
+  - One man is working on a wrist watch to repair it.
+  - A white male dog is following a black female dog because she is in heat.
+- source_sentence: A woman in a white shirt and red jeans is carrying a plastic bag
+    and cellphone while walking along the street by art prints.
+  sentences:
+  - The people are sitting on a couch
+  - The man is walking down the street with a plastic bag.
+  - A man wants to join in the conversation
+- source_sentence: Girl in a thin rowboat leaving the dock of a lake.
+  sentences:
+  - A man in a solid white shirt and two black-haired boys pose for pictures inside.
+  - The ladies are having a conversation.
+  - The girl is sitting on the shore of the lake.
+- source_sentence: A large crowd watches as a couple tap dances together on a wooden
+    floor.
+  sentences:
+  - People are leaving the restaurant.
+  - A man crashes his car into the grocery store.
+  - A man swings a golf club.
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: snli dev
+      type: snli-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.5007411996817115
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.49310662404125943
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.4737846265333258
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.4923216703895389
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.47496147875492195
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.4931066240443629
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.500741200773276
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.49310655847757945
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.500741200773276
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.4931066240443629
+      name: Spearman Max
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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': 128, '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("Nessrine9/finetuned2-MiniLM-L12-v2")
+# Run inference
+sentences = [
+    'A large crowd watches as a couple tap dances together on a wooden floor.',
+    'A man swings a golf club.',
+    'A man crashes his car into the grocery store.',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `snli-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.5007     |
+| spearman_cosine    | 0.4931     |
+| pearson_manhattan  | 0.4738     |
+| spearman_manhattan | 0.4923     |
+| pearson_euclidean  | 0.475      |
+| spearman_euclidean | 0.4931     |
+| pearson_dot        | 0.5007     |
+| spearman_dot       | 0.4931     |
+| pearson_max        | 0.5007     |
+| **spearman_max**   | **0.4931** |
+
+<!--
+## 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: 100,000 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: 7 tokens</li><li>mean: 16.85 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.61 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                             | sentence_1                                                             | label            |
+  |:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------|
+  | <code>A biker is practicing a trick while his friend watch him as his audience.</code> | <code>man riding the bike to show his talent to his girlfriend.</code> | <code>0.5</code> |
+  | <code>A man in a brown jacket standing in front of an open porch door.</code>          | <code>A man is standing in front of the porch door.</code>             | <code>0.0</code> |
+  | <code>Two men and three children are at the beach.</code>                              | <code>Five people enjoying their vacation.</code>                      | <code>0.5</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 4
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 4
+- `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`: 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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | snli-dev_spearman_max |
+|:------:|:-----:|:-------------:|:---------------------:|
+| 0.08   | 500   | 0.1807        | 0.3001                |
+| 0.16   | 1000  | 0.1497        | 0.3646                |
+| 0.24   | 1500  | 0.1443        | 0.3652                |
+| 0.32   | 2000  | 0.1394        | 0.3860                |
+| 0.4    | 2500  | 0.1369        | 0.3810                |
+| 0.48   | 3000  | 0.1346        | 0.3895                |
+| 0.56   | 3500  | 0.1358        | 0.4147                |
+| 0.64   | 4000  | 0.1387        | 0.4190                |
+| 0.72   | 4500  | 0.131         | 0.4254                |
+| 0.8    | 5000  | 0.1314        | 0.4219                |
+| 0.88   | 5500  | 0.1288        | 0.4342                |
+| 0.96   | 6000  | 0.1299        | 0.4135                |
+| 1.0    | 6250  | -             | 0.4393                |
+| 1.04   | 6500  | 0.1306        | 0.4565                |
+| 1.12   | 7000  | 0.1253        | 0.4433                |
+| 1.2    | 7500  | 0.1275        | 0.4486                |
+| 1.28   | 8000  | 0.1265        | 0.4616                |
+| 1.3600 | 8500  | 0.1237        | 0.4462                |
+| 1.44   | 9000  | 0.1223        | 0.4573                |
+| 1.52   | 9500  | 0.123         | 0.4609                |
+| 1.6    | 10000 | 0.1251        | 0.4678                |
+| 1.6800 | 10500 | 0.1262        | 0.4500                |
+| 1.76   | 11000 | 0.1194        | 0.4696                |
+| 1.8400 | 11500 | 0.1206        | 0.4733                |
+| 1.92   | 12000 | 0.118         | 0.4701                |
+| 2.0    | 12500 | 0.1238        | 0.4688                |
+| 2.08   | 13000 | 0.1191        | 0.4646                |
+| 2.16   | 13500 | 0.1179        | 0.4757                |
+| 2.24   | 14000 | 0.1177        | 0.4652                |
+| 2.32   | 14500 | 0.1176        | 0.4873                |
+| 2.4    | 15000 | 0.115         | 0.4674                |
+| 2.48   | 15500 | 0.1141        | 0.4784                |
+| 2.56   | 16000 | 0.1143        | 0.4824                |
+| 2.64   | 16500 | 0.1184        | 0.4898                |
+| 2.7200 | 17000 | 0.1124        | 0.4818                |
+| 2.8    | 17500 | 0.1141        | 0.4905                |
+| 2.88   | 18000 | 0.1115        | 0.4850                |
+| 2.96   | 18500 | 0.1123        | 0.4867                |
+| 3.0    | 18750 | -             | 0.4867                |
+| 3.04   | 19000 | 0.1149        | 0.4849                |
+| 3.12   | 19500 | 0.1114        | 0.4888                |
+| 3.2    | 20000 | 0.1124        | 0.4903                |
+| 3.2800 | 20500 | 0.1124        | 0.4900                |
+| 3.36   | 21000 | 0.1088        | 0.4871                |
+| 3.44   | 21500 | 0.1065        | 0.4835                |
+| 3.52   | 22000 | 0.1075        | 0.4912                |
+| 3.6    | 22500 | 0.1115        | 0.4944                |
+| 3.68   | 23000 | 0.1122        | 0.4932                |
+| 3.76   | 23500 | 0.1074        | 0.4917                |
+| 3.84   | 24000 | 0.1081        | 0.4923                |
+| 3.92   | 24500 | 0.1057        | 0.4921                |
+| 4.0    | 25000 | 0.1118        | 0.4931                |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## 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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L12-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": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:38da6d8a7c41426c37bbcae56d420e5cbd8fe08f5902b9ea64f5f29aad62a5fe
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

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
+  }
+}

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
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 128,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}