Add new SentenceTransformer model.

31ce749 verified 9 months ago

19.3 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:1M<n<10M
	- loss:MSELoss
	base_model: l3cube-pune/indic-sentence-similarity-sbert
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	- negative_mse
	widget:
	- source_sentence: Nobody is standing
	sentences:
	- The person staring has no vision.
	- The person in black T-shirt is sitting.
	- The two girls are at the amusement park.
	- source_sentence: The door is open.
	sentences:
	- A child is looking out of a door.
	- a woman is shopping by fisher's popcorn
	- Team owner, president and head coach Don Sims is a Christian.
	- source_sentence: A man is jogging.
	sentences:
	- A man is rock climbing with protective rope.
	- There is a Coca-Cola sign on a building.
	- A group of women are selling their wares
	- source_sentence: A woman is outside
	sentences:
	- A girl is posing outside.
	- A woman is having a drink with a friend.
	- The man is sitting on Santa's lap.
	- source_sentence: Men are outdoors.
	sentences:
	- A man is outside.
	- A Little girl is enjoying cake outside.
	- The child is dancing inside.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.6061168880496322
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.63159627628102
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.4867734432158827
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.5132315973464433
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.5060055860550953
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.530647353370298
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.2197998289852973
	name: Pearson Dot
	- type: spearman_dot
	value: 0.2098437681521414
	name: Spearman Dot
	- type: pearson_max
	value: 0.6061168880496322
	name: Pearson Max
	- type: spearman_max
	value: 0.63159627628102
	name: Spearman Max
	- task:
	type: knowledge-distillation
	name: Knowledge Distillation
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: negative_mse
	value: -3.0273379758000374
	name: Negative Mse
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.7908829263963781
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7964877056053918
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7759961128627
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7730137991653084
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7764317252322528
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7735945428555226
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6958642398985296
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6842506896957747
	name: Spearman Dot
	- type: pearson_max
	value: 0.7908829263963781
	name: Pearson Max
	- type: spearman_max
	value: 0.7964877056053918
	name: Spearman Max
	---

	# SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert). It maps sentences & paragraphs to a 768-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: [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) <!-- at revision b07ef91a96390f3e35ce94ddb42340861519bf07 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- Language: en
	<!-- - 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: BertModel
	(1): Pooling({'word_embedding_dimension': 768, '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("ammumadhu/Indic_Bert-8-layers")
	# Run inference
	sentences = [
	'Men are outdoors.',
	'A man is outside.',
	'A Little girl is enjoying cake outside.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# 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: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6061 \|
	\| spearman_cosine \| 0.6316 \|
	\| pearson_manhattan \| 0.4868 \|
	\| spearman_manhattan \| 0.5132 \|
	\| pearson_euclidean \| 0.506 \|
	\| spearman_euclidean \| 0.5306 \|
	\| pearson_dot \| 0.2198 \|
	\| spearman_dot \| 0.2098 \|
	\| pearson_max \| 0.6061 \|
	\| spearman_max \| 0.6316 \|

	#### Knowledge Distillation

	* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

	\| Metric \| Value \|
	\|:-----------------\|:------------\|
	\| negative_mse \| -3.0273 \|

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7909 \|
	\| spearman_cosine \| 0.7965 \|
	\| pearson_manhattan \| 0.776 \|
	\| spearman_manhattan \| 0.773 \|
	\| pearson_euclidean \| 0.7764 \|
	\| spearman_euclidean \| 0.7736 \|
	\| pearson_dot \| 0.6959 \|
	\| spearman_dot \| 0.6843 \|
	\| pearson_max \| 0.7909 \|
	\| spearman_max \| 0.7965 \|

	<!--
	## 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: 1,147,385 training samples
	* Columns: <code>sentence</code> and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| list \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 12.59 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \|
	* Samples:
	\| sentence \| label \|
	\|:---------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>[-0.0009042086312547326, 0.02319158799946308, 0.016657305881381035, -0.004571350757032633, -0.008184989914298058, ...]</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>[-0.020024249330163002, -0.0005705401999875903, 0.025419672951102257, -0.014105383306741714, 0.009407470934092999, ...]</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>[-0.01713346689939499, -2.3264645278686658e-05, -0.0005397812929004431, 0.002506087301298976, 0.027286207303404808, ...]</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

	### Evaluation Dataset

	#### sentence-transformers/wikipedia-en-sentences

	* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
	* Size: 10,000 evaluation samples
	* Columns: <code>sentence</code> and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| list \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 13.53 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \|
	* Samples:
	\| sentence \| label \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>[-0.000599742284975946, 0.0042074089869856834, 0.0013686479069292545, -0.0009170330595225096, -0.010106148198246956, ...]</code> \|
	\| <code>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.</code> \| <code>[0.003711540251970291, -0.005768307950347662, -0.03475787863135338, 0.010626137256622314, -0.0023863380774855614, ...]</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>[-0.014246350154280663, 0.015385480597615242, 0.0016394935082644224, -0.013386472128331661, -0.015061145648360252, ...]</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 0.0001
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: 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
	- `learning_rate`: 0.0001
	- `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`: 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`: 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`: 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
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| negative_mse \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:------------:\|:-----------------------:\|:------------------------:\|
	\| 0 \| 0 \| - \| -3.0273 \| 0.6316 \| - \|
	\| 0.2231 \| 1000 \| 0.0015 \| - \| - \| - \|
	\| 0.4462 \| 2000 \| 0.0001 \| - \| - \| - \|
	\| 0.6693 \| 3000 \| 0.0001 \| - \| - \| - \|
	\| 0.8925 \| 4000 \| 0.0001 \| - \| - \| - \|
	\| 1.0 \| 4482 \| - \| - \| - \| 0.7965 \|


	### Framework Versions
	- Python: 3.10.14
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.2
	- PyTorch: 2.1.0
	- Accelerate: 0.30.1
	- Datasets: 2.19.2
	- 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",
	}
	```

	#### MSELoss
	```bibtex
	@inproceedings{reimers-2020-multilingual-sentence-bert,
	title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2020",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/2004.09813",
	}
	```

	<!--
	## 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.
	-->