Updating model with the latest version

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "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

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+---
+base_model: FacebookAI/roberta-large-mnli
+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:72338
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: Do I need to know HTML & CSS to learn javascript?
+  sentences:
+  - What Would the Piano Chords to "Winter, You Tease" by Layla be?
+  - Men playing a sport outside.
+  - How do I learn web development as quickly as possible?
+- source_sentence: Isn't it inconsistent to prefer both a well-informed electorate
+    and an ignorant jury?
+  sentences:
+  - Some people like when the electorate is stupid.
+  - Two people working on computer
+  - How is 0+0+0+0+0+0+0…= undefined?
+- source_sentence: A fluffy white and brown puppy is playing with a white, curly-haired
+    puppy.
+  sentences:
+  - Why is H2O liquid and H2S solid at room temperature?
+  - The bird is sitting in a nest.
+  - The puppies are playing together.
+- source_sentence: A woman in a blue shirt and sunglasses dancing.
+  sentences:
+  - The woman is dancing.
+  - Is Qatar part of UAE?
+  - Two lovers walk together in Paris.
+- source_sentence: A motorbike rider is barreling across a grass lawn.
+  sentences:
+  - The girl is wearing a shirt.
+  - Why doesn't Java have pointers?
+  - The rider is outdoors on a motorbike.
+model-index:
+- name: SentenceTransformer based on FacebookAI/roberta-large-mnli
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: pearson_cosine
+      value: 0.8457307745816387
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.810079801718123
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8108388961642436
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7916598710432559
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8106363007947738
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.7916399795577503
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.8566895266416593
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.8163029561419852
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8566895266416593
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8163029561419852
+      name: Spearman Max
+---
+
+# SentenceTransformer based on FacebookAI/roberta-large-mnli
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-large-mnli](https://huggingface.co/FacebookAI/roberta-large-mnli). It maps sentences & paragraphs to a 1024-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:** [FacebookAI/roberta-large-mnli](https://huggingface.co/FacebookAI/roberta-large-mnli) <!-- at revision 2a8f12d27941090092df78e4ba6f0928eb5eac98 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+)
+```
+
+## 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("richie-ghost/sbert_facebook_large_mnli_openVino2")
+# Run inference
+sentences = [
+    'A motorbike rider is barreling across a grass lawn.',
+    'The rider is outdoors on a motorbike.',
+    'The girl is wearing a shirt.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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: `eval`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.8457     |
+| spearman_cosine    | 0.8101     |
+| pearson_manhattan  | 0.8108     |
+| spearman_manhattan | 0.7917     |
+| pearson_euclidean  | 0.8106     |
+| spearman_euclidean | 0.7916     |
+| pearson_dot        | 0.8567     |
+| spearman_dot       | 0.8163     |
+| pearson_max        | 0.8567     |
+| **spearman_max**   | **0.8163** |
+
+<!--
+## 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: 72,338 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                                                                            | int                                             |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 18.11 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.82 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>0: ~50.70%</li><li>1: ~49.30%</li></ul> |
+* Samples:
+  | sentence_0                                                                                                   | sentence_1                                                                                                                                                       | label          |
+  |:-------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Hows would you create strategies and tactics in various combat situations?</code>                      | <code>I have girlfriend and their parents accepted for my marriage, I m working in Nagpur but her parents wanted me to shift Bangalore? Is it valid wish?</code> | <code>0</code> |
+  | <code>Man from the army speaking with civilian women.</code>                                                 | <code>The man is a sergeant</code>                                                                                                                               | <code>0</code> |
+  | <code>An old man with a white shirt and black pants sits on a chair in the opening of a stone tunnel.</code> | <code>Someone has black pants.</code>                                                                                                                            | <code>1</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
+- `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`: 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}
+- `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 | eval_spearman_max |
+|:------:|:-----:|:-------------:|:-----------------:|
+| 0.1106 | 500   | 0.1845        | 0.6681            |
+| 0.2211 | 1000  | 0.0942        | 0.7711            |
+| 0.3317 | 1500  | 0.0821        | 0.6355            |
+| 0.4423 | 2000  | 0.0794        | 0.7283            |
+| 0.5529 | 2500  | 0.0788        | 0.7129            |
+| 0.6634 | 3000  | 0.0737        | 0.7853            |
+| 0.7740 | 3500  | 0.07          | 0.7013            |
+| 0.8846 | 4000  | 0.0686        | 0.7809            |
+| 0.9951 | 4500  | 0.0683        | 0.7578            |
+| 1.0    | 4522  | -             | 0.7976            |
+| 1.1057 | 5000  | 0.07          | 0.7749            |
+| 1.2163 | 5500  | 0.0656        | 0.7826            |
+| 1.3268 | 6000  | 0.0587        | 0.8032            |
+| 1.4374 | 6500  | 0.0584        | 0.7666            |
+| 1.5480 | 7000  | 0.0582        | 0.7917            |
+| 1.6586 | 7500  | 0.0546        | 0.7945            |
+| 1.7691 | 8000  | 0.0528        | 0.7786            |
+| 1.8797 | 8500  | 0.051         | 0.7732            |
+| 1.9903 | 9000  | 0.0527        | 0.7996            |
+| 2.0    | 9044  | -             | 0.7898            |
+| 2.1008 | 9500  | 0.0509        | 0.7957            |
+| 2.2114 | 10000 | 0.0492        | 0.7988            |
+| 2.3220 | 10500 | 0.0451        | 0.8044            |
+| 2.4326 | 11000 | 0.0443        | 0.7961            |
+| 2.5431 | 11500 | 0.0445        | 0.7975            |
+| 2.6537 | 12000 | 0.0433        | 0.8054            |
+| 2.7643 | 12500 | 0.0394        | 0.7890            |
+| 2.8748 | 13000 | 0.0387        | 0.8020            |
+| 2.9854 | 13500 | 0.0401        | 0.8096            |
+| 3.0    | 13566 | -             | 0.8087            |
+| 3.0960 | 14000 | 0.0399        | 0.8098            |
+| 3.2065 | 14500 | 0.039         | 0.8077            |
+| 3.3171 | 15000 | 0.0346        | 0.8021            |
+| 3.4277 | 15500 | 0.0339        | 0.8082            |
+| 3.5383 | 16000 | 0.0347        | 0.8150            |
+| 3.6488 | 16500 | 0.0352        | 0.8144            |
+| 3.7594 | 17000 | 0.032         | 0.8141            |
+| 3.8700 | 17500 | 0.0326        | 0.8151            |
+| 3.9805 | 18000 | 0.0318        | 0.8162            |
+| 4.0    | 18088 | -             | 0.8163            |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- 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 +1,38 @@
+{
+  "_name_or_path": "/content/drive/MyDrive/SBERT-FineTuning2/Facebook-Large/save_pretrained",
+  "_num_labels": 3,
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "CONTRADICTION",
+    "1": "NEUTRAL",
+    "2": "ENTAILMENT"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "CONTRADICTION": 0,
+    "ENTAILMENT": 2,
+    "NEUTRAL": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

openvino/openvino_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77696135dcf025efaeb60199f09551216e65bcbe1c4fb831a87383661313920f
+size 1417244820

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}