{
"info": {
"author": "sean lee",
"author_email": "xmlee97@gmail.com",
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"Development Status :: 5 - Production/Stable",
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"License :: OSI Approved :: BSD License",
"Natural Language :: English",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
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"description": "EN | [\u7b80\u4f53\u4e2d\u6587](README_zh.md) \n\n# AnglE\ud83d\udcd0\n\n> It is Angle \ud83d\udcd0, not Angel \ud83d\udc7c.\n\n**For more detailed usage, please read the \ud83d\udcd8 document:** https://angle.readthedocs.io/en/latest/index.html\n\n\n ![\"https://arxiv.org/abs/2309.12871\"](\"https://img.shields.io/badge/Arxiv-2309.12871-yellow.svg?style=flat-square\")
\n\n\n ![\"PyPI](\"https://img.shields.io/pypi/v/angle_emb?style=flat-square\")
\n\n\n ![\"PyPI](\"https://img.shields.io/pypi/dm/angle_emb?style=flat-square\")
\n\n\n ![\"Read](\"https://readthedocs.org/projects/angle/badge/?version=latest&style=flat-square\")
\n\n\n\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sick-r-1?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts16?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts15?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts14?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts13?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts12?p=angle-optimized-text-embeddings)\n[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark?p=angle-optimized-text-embeddings)\n\n\ud83d\udce2 **Train/Infer Powerful Sentence Embeddings with AnglE.**\nThis library is from the paper: [AnglE: Angle-optimized Text Embeddings](https://arxiv.org/abs/2309.12871). It allows for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. AnglE is also a general sentence embedding inference framework, allowing for infering a variety of transformer-based sentence embeddings.\n\n## \u2728 Features\n\n**Loss**:\n- \ud83d\udcd0 AnglE loss\n- \u2696 Contrastive loss\n- \ud83d\udccf CoSENT loss\n- \u2615\ufe0f Espresso loss (previously known as 2DMSE, detail: [README_ESE](README_ESE.md))\n\n**Backbones**:\n- BERT-based models (BERT, RoBERTa, ELECTRA, ALBERT, etc.)\n- LLM-based models (LLaMA, Mistral, Qwen, etc.)\n- Bi-directional LLM-based models (LLaMA, Mistral, Qwen, OpenELMo, etc.. refer to: https://github.com/WhereIsAI/BiLLM)\n\n**Training**:\n- Single-GPU training\n- Multi-GPU training\n\n\n> ![\"http://makeapullrequest.com\"](\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square\")
\n More features will be added in the future. \n\n## \ud83c\udfc6 Achievements\n\n\ud83d\udcc5 May 16, 2024 | Paper \"[AnglE: Angle-optimized Text Embeddings](https://arxiv.org/abs/2309.12871)\" is accepted by ACL 2024 Main Conference.\n\n\ud83d\udcc5 Mar 13, 2024 | Paper \"[BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings](https://arxiv.org/abs/2311.05296)\" is accepted by NAACL 2024 Main Conference.\n\n\n\ud83d\udcc5 Mar 8, 2024 | \ud83c\udf5e [mixedbread's embedding](https://www.mixedbread.ai/blog/mxbai-embed-large-v1) ([mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)) achieves SOTA on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of **64.68**! The model is trained using AnglE. Congrats mixedbread!\n\n\n\ud83d\udcc5 Dec 4, 2023 | Our universal sentence embedding [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) achieves SOTA on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of **64.64**! The model is trained using AnglE.\n\n\n\ud83d\udcc5 Dec, 2023 | AnglE achieves SOTA performance on the STS Bechmark Semantic Textual Similarity! \n\n\n## \ud83e\udd17 Official Pretrained Models\n\nBERT-based models:\n\n| \ud83e\udd17 HF | Max Tokens | Pooling Strategy | Scenario |\n|----|------|------|------|\n| [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) | 512 | cls | English, General-purpose |\n| [WhereIsAI/UAE-Code-Large-V1](https://huggingface.co/WhereIsAI/UAE-Code-Large-V1) | 512 | cls | Code Similarity |\n\nLLM-based models:\n\n| \ud83e\udd17 HF (lora weight) | Backbone | Max Tokens | Prompts | Pooling Strategy | Scenario |\n|----|------|------|------|------|------|\n| [SeanLee97/angle-llama-13b-nli](https://huggingface.co/SeanLee97/angle-llama-13b-nli) | NousResearch/Llama-2-13b-hf | 4096 | `Prompts.A` | last token | English, Similarity Measurement | \n| [SeanLee97/angle-llama-7b-nli-v2](https://huggingface.co/SeanLee97/angle-llama-7b-nli-v2) | NousResearch/Llama-2-7b-hf | 4096 | `Prompts.A` | last token | English, Similarity Measurement | \n\n\n**\ud83d\udca1 You can find more third-party embeddings trained with AnglE in [HuggingFace Collection](https://huggingface.co/collections/SeanLee97/angle-based-embeddings-669a181354729d168a6ead9b)**\n\n\n## \ud83d\ude80 Quick Start\n\n### \u2b07\ufe0f Installation\n\n```bash\npython -m pip install -U angle-emb\n```\n\n### \u231b Infer BERT-based Model\n[](https://colab.research.google.com/drive/1QJcA2Mvive4pBxWweTpZz9OgwvE42eJZ?usp=sharing)\n\n\n1) **With Prompts**: You can specify a prompt with `prompt=YOUR_PROMPT` in `encode` method. If set a prompt, the inputs should be a list of dict or a single dict with key `text`, where `text` is the placeholder in the prompt for the input text. You can use other placeholder names. We provide a set of predefined prompts in `Prompts` class, you can check them via `Prompts.list_prompts()`.\n\n```python\nfrom angle_emb import AnglE, Prompts\nfrom angle_emb.utils import cosine_similarity\n\n\nangle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()\n# For retrieval tasks, we use `Prompts.C` as the prompt for the query when using UAE-Large-V1 (no need to specify prompt for documents).\n# When specify prompt, the inputs should be a list of dict with key 'text'\nqv = angle.encode({'text': 'what is the weather?'}, to_numpy=True, prompt=Prompts.C)\ndoc_vecs = angle.encode([\n 'The weather is great!',\n 'it is rainy today.',\n 'i am going to bed'\n], to_numpy=True)\n\nfor dv in doc_vecs:\n print(cosine_similarity(qv[0], dv))\n```\n\n2) **Without Prompts**: no need to specify a prompt. Just input a list of strings or a single string.\n\n```python\nfrom angle_emb import AnglE\nfrom angle_emb.utils import cosine_similarity\n\n\nangle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()\n# for non-retrieval tasks, we don't need to specify prompt when using UAE-Large-V1.\ndoc_vecs = angle.encode([\n 'The weather is great!',\n 'The weather is very good!',\n 'i am going to bed'\n])\n\nfor i, dv1 in enumerate(doc_vecs):\n for dv2 in doc_vecs[i+1:]:\n print(cosine_similarity(dv1, dv2))\n```\n\n\n### \u231b Infer LLM-based Models\n[](https://colab.research.google.com/drive/1QJcA2Mvive4pBxWweTpZz9OgwvE42eJZ?usp=sharing)\n\nIf the pretrained weight is a LoRA-based model, you need to specify the backbone via `model_name_or_path` and specify the LoRA path via the `pretrained_lora_path` in `from_pretrained` method. \n\n```python\nimport torch\nfrom angle_emb import AnglE, Prompts\nfrom angle_emb.utils import cosine_similarity\n\nangle = AnglE.from_pretrained('NousResearch/Llama-2-7b-hf',\n pretrained_lora_path='SeanLee97/angle-llama-7b-nli-v2',\n pooling_strategy='last',\n is_llm=True,\n torch_dtype=torch.float16).cuda()\nprint('All predefined prompts:', Prompts.list_prompts())\ndoc_vecs = angle.encode([\n {'text': 'The weather is great!'},\n {'text': 'The weather is very good!'},\n {'text': 'i am going to bed'}\n], prompt=Prompts.A)\n\nfor i, dv1 in enumerate(doc_vecs):\n for dv2 in doc_vecs[i+1:]:\n print(cosine_similarity(dv1, dv2))\n```\n\n\n### \u231b Infer BiLLM-based Models\n[](https://colab.research.google.com/drive/1QJcA2Mvive4pBxWweTpZz9OgwvE42eJZ?usp=sharing)\n\nSpecify `apply_billm` and `billm_model_class` to load and infer billm models\n\n\n```python\nimport os\n# set an environment variable for billm start index\nos.environ['BiLLM_START_INDEX'] = '31'\n\nimport torch\nfrom angle_emb import AnglE, Prompts\nfrom angle_emb.utils import cosine_similarity\n\n# specify `apply_billm` and `billm_model_class` to load billm models\nangle = AnglE.from_pretrained('NousResearch/Llama-2-7b-hf',\n pretrained_lora_path='SeanLee97/bellm-llama-7b-nli',\n pooling_strategy='last',\n is_llm=True,\n apply_billm=True,\n billm_model_class='LlamaForCausalLM',\n torch_dtype=torch.float16).cuda()\n\ndoc_vecs = angle.encode([\n {'text': 'The weather is great!'},\n {'text': 'The weather is very good!'},\n {'text': 'i am going to bed'}\n], prompt='The representative word for sentence {text} is:\"')\n\nfor i, dv1 in enumerate(doc_vecs):\n for dv2 in doc_vecs[i+1:]:\n print(cosine_similarity(dv1, dv2))\n```\n\n\n### \u231b Infer Espresso/Matryoshka Models\n[](https://colab.research.google.com/drive/1QJcA2Mvive4pBxWweTpZz9OgwvE42eJZ?usp=sharing)\n\nSpecify `layer_index` and `embedding_size` to truncate embeddings.\n\n\n```python\nfrom angle_emb import AnglE\nfrom angle_emb.utils import cosine_similarity\n\n\nangle = AnglE.from_pretrained('mixedbread-ai/mxbai-embed-2d-large-v1', pooling_strategy='cls').cuda()\n# specify layer_index and embedding size to truncate embeddings\ndoc_vecs = angle.encode([\n 'The weather is great!',\n 'The weather is very good!',\n 'i am going to bed'\n], layer_index=22, embedding_size=768)\n\nfor i, dv1 in enumerate(doc_vecs):\n for dv2 in doc_vecs[i+1:]:\n print(cosine_similarity(dv1, dv2))\n```\n\n### \u231b Infer Third-party Models\n\nYou can load any transformer-based third-party models such as `mixedbread-ai/mxbai-embed-large-v1`, `sentence-transformers/all-MiniLM-L6-v2`, and `BAAI/bge-large-en-v1.5` using `angle_emb`.\n\nHere is an example:\n\n```python\nfrom angle_emb import AnglE\n\nmodel = AnglE.from_pretrained('mixedbread-ai/mxbai-embed-large-v1', pooling_strategy='cls').cuda()\nvec = model.encode('hello world', to_numpy=True)\nprint(vec)\n```\n\n\n## \ud83d\udd78\ufe0f Custom Train\n\n### \ud83d\uddc2\ufe0f 1. Data Prepation\n\nWe currently support three dataset formats:\n\n1) `DatasetFormats.A`: it is a pair format with three columns: `text1`, `text2`, and `label` (0/1).\n\n2) `DatasetFormats.B`: it is a triple format with three columns: `text`, `positive`, and `negative`. `positive` and `negative` store the positive and negative samples of `text`.\n\n3) `DatasetFormats.C`: it is a pair format with two columns: `text`, `positive`. `positive` store the positive sample of `text`.\n\nYou need to prepare your data into huggingface `datasets.Dataset` in one of the formats in terms of your supervised data.\n\n### \ud83d\ude82 2. Train with CLI\n\nUse `angle-trainer` to train your AnglE model in cli mode. \n\n1) Single gpu training:\n\nUsage: \n\n```bash\nCUDA_VISIBLE_DEVICES=0 angle-trainer --help\n```\n\n2) Multi-gpu training:\n\nUsage:\n\n```bash\nCUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 -m angle_emb.angle_trainer --help\n```\n\n### \ud83d\ude82 3. Custom Train\n\n[](https://colab.research.google.com/drive/1h28jHvv_x-0fZ0tItIMjf8rJGp3GcO5V?usp=sharing)\n\n\n```python\nfrom datasets import load_dataset\nfrom angle_emb import AnglE, AngleDataTokenizer\n\n\n# 1. load pretrained model\nangle = AnglE.from_pretrained('SeanLee97/angle-bert-base-uncased-nli-en-v1', max_length=128, pooling_strategy='cls').cuda()\n\n# 2. load dataset\n# `text1`, `text2`, and `label` are three required columns.\nds = load_dataset('mteb/stsbenchmark-sts')\nds = ds.map(lambda obj: {\"text1\": str(obj[\"sentence1\"]), \"text2\": str(obj['sentence2']), \"label\": obj['score']})\nds = ds.select_columns([\"text1\", \"text2\", \"label\"])\n\n# 3. transform data\ntrain_ds = ds['train'].shuffle().map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)\nvalid_ds = ds['validation'].map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)\n\n# 4. fit\nangle.fit(\n train_ds=train_ds,\n valid_ds=valid_ds,\n output_dir='ckpts/sts-b',\n batch_size=32,\n epochs=5,\n learning_rate=2e-5,\n save_steps=100,\n eval_steps=1000,\n warmup_steps=0,\n gradient_accumulation_steps=1,\n loss_kwargs={\n 'cosine_w': 1.0,\n 'ibn_w': 1.0,\n 'angle_w': 1.0,\n 'cosine_tau': 20,\n 'ibn_tau': 20,\n 'angle_tau': 20\n },\n fp16=True,\n logging_steps=100\n)\n\n# 5. evaluate\ncorrcoef = angle.evaluate(ds['test'])\nprint('Spearman\\'s corrcoef:', corrcoef)\n```\n\n### \ud83d\udca1 Others\n\n- To enable `llm` training, please specify `--is_llm 1` and configure appropriate LoRA hyperparameters.\n- To enable `billm` training, please specify `--apply_billm 1` and configure appropriate `billm_model_class` such as `LLamaForCausalLM` (refer to: https://github.com/WhereIsAI/BiLLM?tab=readme-ov-file#usage).\n- To enable espresso sentence embeddings (ESE), please specify `--apply_ese 1` and configure appropriate ESE hyperparameters via `--ese_kl_temperature float` and `--ese_compression_size integer`.\n- To convert the trained AnglE models to `sentence-transformers`, please run `python scripts/convert_to_sentence_transformers.py --help` for more details.\n\n\n## \ud83d\udca1 4. Fine-tuning Tips\n\n1\ufe0f\u20e3 If your dataset format is `DatasetFormats.A`, it is recommended to slightly increase the weight for `cosine_w` or slightly decrease the weight for `ibn_w`.\n\n2\ufe0f\u20e3 If your dataset format is `DatasetFormats.B`, it is recommended to set `cosine_w` to 0, and increase the weight for `ibn_w` such as 10 and 20. The `angle_tau` is recommended to set to 20.0.\n\n3\ufe0f\u20e3 If your dataset format is `DatasetFormats.C`, only `ibn_w` and `ibn_tau` are effective. You don't need to tune other parameters.\n\n4\ufe0f\u20e3 To alleviate information forgetting in fine-tuning, it is better to specify the `teacher_name_or_path`. If the `teacher_name_or_path` equals `model_name_or_path`, it will conduct self-distillation. **It is worth to note that** `teacher_name_or_path` has to have the same tokenizer as `model_name_or_path`. Or it will lead to unexpected results.\n\n\n# \ud83e\udee1 Citation\n\nYou are welcome to use our code and pre-trained models. If you use our code and pre-trained models, please support us by citing our work as follows:\n\n```bibtex\n@article{li2023angle,\n title={AnglE-optimized Text Embeddings},\n author={Li, Xianming and Li, Jing},\n journal={arXiv preprint arXiv:2309.12871},\n year={2023}\n}\n```\n\n# \ud83d\udcdc ChangeLogs\n\n| \ud83d\udcc5 | Description |\n|----|------|\n| 2024 May 21 | support Espresso Sentence Embeddings |\n| 2024 Feb 7 | support training with only positive pairs (`DatasetFormats.C`) |\n| 2023 Dec 4 | Release a universal English sentence embedding model: [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) |\n| 2023 Nov 2 | Release an English pretrained model: `SeanLee97/angle-llama-13b-nli` |\n| 2023 Oct 28 | Release two chinese pretrained models: `SeanLee97/angle-roberta-wwm-base-zhnli-v1` and `SeanLee97/angle-llama-7b-zhnli-v1`; Add chinese README.md |\n\n# \ud83d\udce7 Contact\n\nIf you have any questions or suggestions, please feel free to contact us via email: xmlee97@gmail.com\n\n# \u00a9 License\n\nThis project is licensed under the MIT License.\nFor the pretrained models, please refer to the corresponding license of the models.\n",
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