sachin19566

Add new SentenceTransformer model.

bec4a3e verified about 1 month ago

16.2 kB

	---
	base_model: BAAI/bge-base-en-v1.5
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:3683
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: Cost Accounting - A Comprehensive Study
	sentences:
	- Beginner Level
	- Business Finance
	- All Levels
	- source_sentence: Build Financial Models & Value Companies The Easy Way
	sentences:
	- All Levels
	- Business Finance
	- All Levels
	- source_sentence: build a solid foundation for trading options
	sentences:
	- Intermediate Level
	- Business Finance
	- All Levels
	- source_sentence: Create Beautiful Image Maps for Your Website
	sentences:
	- Graphic Design
	- Intermediate Level
	- All Levels
	- source_sentence: 'Multiply your returns using ''Value Investing",https://www.udemy.com/multiply-your-returns-using-value-investing/,true,20,1942,19,63,All
	Levels,4.5 hours,2015-07-23T00:08:33Z

	874284,Weekly Forex Analysis by Baraq FX"'
	sentences:
	- Beginner Level
	- Business Finance
	- All Levels
	---

	# SentenceTransformer based on BAAI/bge-base-en-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 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': True}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sachin19566/bge-base-en-v1.5-udemy-fte")
	# Run inference
	sentences = [
	'Multiply your returns using \'Value Investing",https://www.udemy.com/multiply-your-returns-using-value-investing/,true,20,1942,19,63,All Levels,4.5 hours,2015-07-23T00:08:33Z\n874284,Weekly Forex Analysis by Baraq FX"',
	'All Levels',
	'Business Finance',
	]
	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]
	```

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	<details><summary>Click to expand</summary>

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 3,683 training samples
	* Columns: <code>course_title</code>, <code>level</code>, and <code>subject</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| course_title \| level \| subject \|
	\|:--------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 11.02 tokens</li><li>max: 81 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 4.27 tokens</li><li>max: 5 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> \|
	* Samples:
	\| course_title \| level \| subject \|
	\|:-------------------------------------------------------------------------\|:--------------------------------\|:------------------------------\|
	\| <code>Ultimate Investment Banking Course</code> \| <code>All Levels</code> \| <code>Business Finance</code> \|
	\| <code>Complete GST Course & Certification - Grow Your CA Practice</code> \| <code>All Levels</code> \| <code>Business Finance</code> \|
	\| <code>Financial Modeling for Business Analysts and Consultants</code> \| <code>Intermediate Level</code> \| <code>Business Finance</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 100 evaluation samples
	* Columns: <code>course_title</code>, <code>level</code>, and <code>subject</code>
	* Approximate statistics based on the first 100 samples:
	\| \| course_title \| level \| subject \|
	\|:--------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 12.63 tokens</li><li>max: 81 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 4.42 tokens</li><li>max: 5 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> \|
	* Samples:
	\| course_title \| level \| subject \|
	\|:-------------------------------------------------------------------------\|:----------------------------\|:------------------------------\|
	\| <code>Learn to Use jQuery UI Widgets</code> \| <code>Beginner Level</code> \| <code>Web Development</code> \|
	\| <code>Financial Statements: Learn Accounting. Unlock the Numbers.</code> \| <code>Beginner Level</code> \| <code>Business Finance</code> \|
	\| <code>Trade Recap I - A Real Look at Futures Options Markets</code> \| <code>Beginner Level</code> \| <code>Business Finance</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 3e-06
	- `max_steps`: 932
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `batch_sampler`: no_duplicates

	#### 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`: 3e-06
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 3.0
	- `max_steps`: 932
	- `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`: 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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \|
	\|:------:\|:----:\|:-------------:\|:------:\|
	\| 0.0866 \| 20 \| 2.2161 \| 1.7831 \|
	\| 0.1732 \| 40 \| 1.9601 \| 1.5400 \|
	\| 0.2597 \| 60 \| 1.6253 \| 1.1987 \|
	\| 0.3463 \| 80 \| 1.2393 \| 1.0009 \|
	\| 0.4329 \| 100 \| 1.1817 \| 0.9073 \|
	\| 0.5195 \| 120 \| 1.0667 \| 0.8817 \|
	\| 0.6061 \| 140 \| 1.258 \| 0.8282 \|
	\| 0.6926 \| 160 \| 1.2375 \| 0.7618 \|
	\| 0.7792 \| 180 \| 1.0925 \| 0.7274 \|
	\| 0.8658 \| 200 \| 1.0823 \| 0.7101 \|
	\| 0.9524 \| 220 \| 0.8789 \| 0.7056 \|
	\| 1.0390 \| 240 \| 0.9597 \| 0.7107 \|
	\| 1.1255 \| 260 \| 0.8427 \| 0.7221 \|
	\| 1.2121 \| 280 \| 0.8612 \| 0.7287 \|
	\| 1.2987 \| 300 \| 0.8428 \| 0.7275 \|
	\| 1.3853 \| 320 \| 0.6426 \| 0.7451 \|
	\| 1.4719 \| 340 \| 0.709 \| 0.7642 \|
	\| 1.5584 \| 360 \| 0.6602 \| 0.7851 \|
	\| 1.6450 \| 380 \| 0.7356 \| 0.8244 \|
	\| 1.7316 \| 400 \| 0.7633 \| 0.8310 \|
	\| 1.8182 \| 420 \| 0.9592 \| 0.8185 \|
	\| 1.9048 \| 440 \| 0.6715 \| 0.8094 \|
	\| 1.9913 \| 460 \| 0.7926 \| 0.8103 \|
	\| 2.0779 \| 480 \| 0.7703 \| 0.8011 \|
	\| 2.1645 \| 500 \| 0.6287 \| 0.8266 \|
	\| 2.2511 \| 520 \| 0.5481 \| 0.8536 \|
	\| 2.3377 \| 540 \| 0.7101 \| 0.8679 \|
	\| 2.4242 \| 560 \| 0.423 \| 0.9025 \|
	\| 2.5108 \| 580 \| 0.6814 \| 0.9197 \|
	\| 2.5974 \| 600 \| 0.5879 \| 0.9492 \|
	\| 2.6840 \| 620 \| 0.537 \| 0.9861 \|
	\| 2.7706 \| 640 \| 0.5107 \| 1.0179 \|
	\| 2.8571 \| 660 \| 0.6164 \| 1.0413 \|
	\| 2.9437 \| 680 \| 0.6582 \| 1.0710 \|
	\| 3.0303 \| 700 \| 0.4553 \| 1.1001 \|
	\| 3.1169 \| 720 \| 0.3649 \| 1.1416 \|
	\| 3.2035 \| 740 \| 0.9273 \| 1.1142 \|
	\| 3.2900 \| 760 \| 0.8816 \| 1.0694 \|
	\| 3.3766 \| 780 \| 0.7005 \| 1.0481 \|
	\| 3.4632 \| 800 \| 1.9002 \| 1.0289 \|
	\| 3.5498 \| 820 \| 1.4467 \| 1.0141 \|
	\| 3.6364 \| 840 \| 1.5564 \| 1.0023 \|
	\| 3.7229 \| 860 \| 1.2316 \| 0.9961 \|
	\| 3.8095 \| 880 \| 1.0549 \| 0.9931 \|
	\| 3.8961 \| 900 \| 1.2359 \| 0.9913 \|
	\| 3.9827 \| 920 \| 1.3568 \| 0.9897 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.0
	- Transformers: 4.44.2
	- PyTorch: 2.4.0+cu121
	- Accelerate: 0.33.0
	- Datasets: 3.0.0
	- 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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