Updating model with the latest version

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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]
	```

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	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## 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",
	}
	```

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