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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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96
README.md
96
README.md
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# SFR-Embedding-Code-400M_R
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---
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license: cc-by-nc-4.0
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---
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<h1 align="center">Salesforce/SFR-Embedding-Code-400M_R</h1>
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**SFR-Embedding by Salesforce Research.**
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The Salesforce/SFR-Embedding-Code is a generalist embedding model family for multilingual and multi-task code and Text retrieval. It demonstrates superior performance compared to various open-source code embedding models across multiple code retrieval tasks.
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Check out our [paper](https://arxiv.org/abs/2411.12644) for more details!
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### Ethical Considerations
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This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our [AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ExternalFacing_Services_Policy.pdf) and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai-acceptable-use-policy.pdf).
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### License Statement:
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Users need to make their own assessment regarding any obligations or responsibilities under the corresponding licenses or terms and conditions pertaining to the original datasets and data. This release is for research purposes only in support of an academic paper.
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### Performance on CoIR Benchmark
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| Model | Model Size | CoIR AVG (NDCG@10) |
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|-----------------------|------------|---------------------|
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| **SFR-Embedding-Code** | 2B | 67.4 |
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| CodeSage-Large-v2 | 1.3B | 64.2 |
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| CodeSage-Large | 1.3B | 61.0 |
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| **SFR-Embedding-Code** | 400M | 61.9 |
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| CodeRankEmbed | 137M | 60.1 |
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| CodeSage-Base | 356M | 57.5 |
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| Voyage-Code-002 | - | 56.3 |
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| CodeSage-Small | 130M | 54.4 |
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SFR-Embedding Team († indicates co-leaders)
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* Ye Liu
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* Rui Meng
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* Shafiq Rayhan Joty
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* Silvio Savarese
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* Caiming Xiong †
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* Yingbo Zhou †
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* Semih Yavuz †
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## How to run
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#### Transformers
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```python
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import torch.nn.functional as F
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from transformers import AutoModel, AutoTokenizer
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input_texts = [
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"how to implement quick sort in Python?",
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"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
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"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr",
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]
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model_path = 'Salesforce/SFR-Embedding-Code-400M_R'
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
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# Tokenize the input texts
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batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
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outputs = model(**batch_dict)
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embeddings = outputs.last_hidden_state[:, 0]
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# normalize embeddings
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:1] @ embeddings[1:].T) * 100
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print("Similarity Scores:", scores.tolist())
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```
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### Sentence Transformers
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# Requires sentence_transformers>=2.7.0
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```python
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import cos_sim
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sentences = [
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"how to implement quick sort in Python?",
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"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
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"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr",
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]
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model = SentenceTransformer('Salesforce/SFR-Embedding-Code-400M_R', trust_remote_code=True)
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embeddings = model.encode(sentences)
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print(cos_sim(embeddings[0], embeddings[1:]))
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```
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### Citation
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```bibtex
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@article{liu2024codexembed,
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title={CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval},
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author={Liu, Ye and Meng, Rui and Jot, Shafiq and Savarese, Silvio and Xiong, Caiming and Zhou, Yingbo and Yavuz, Semih},
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journal={arXiv preprint arXiv:2411.12644},
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year={2024}
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}
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```
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SFR-Embedding-Code-400M_R
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{
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"_name_or_path": "/export/xgen-embedding/runs/yeliu/train/code_v4/gte-large_text_code_v4_short_addtext_addsql/checkpoints/step_2000",
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"architectures": [
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"NewModel"
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],
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
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"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
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"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
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"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
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"AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
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"AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
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"AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
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},
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-12,
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"layer_norm_type": "layer_norm",
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"logn_attention_clip1": false,
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"logn_attention_scale": false,
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"max_position_embeddings": 8192,
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"model_type": "new",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pack_qkv": true,
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"pad_token_id": 0,
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"position_embedding_type": "rope",
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"rope_scaling": {
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"factor": 2.0,
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"type": "ntk"
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},
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"rope_theta": 160000,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.45.1",
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"type_vocab_size": 2,
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"unpad_inputs": false,
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"use_memory_efficient_attention": false,
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"vocab_size": 30528
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}
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# coding=utf-8
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# Copyright 2024 The GTE Team Authors and Alibaba Group.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" NEW model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class NewConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
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instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the NEW
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[izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"rope"`):
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Type of position embedding. Choose one of `"absolute"`, `"rope"`.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Examples:
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```python
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>>> from transformers import NewConfig, NewModel
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>>> # Initializing a NEW izhx/new-base-en style configuration
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>>> configuration = NewConfig()
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>>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
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>>> model = NewModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "new"
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def __init__(
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self,
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vocab_size=30528,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.0,
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max_position_embeddings=2048,
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type_vocab_size=1,
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initializer_range=0.02,
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layer_norm_type='layer_norm',
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layer_norm_eps=1e-12,
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# pad_token_id=0,
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position_embedding_type="rope",
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rope_theta=10000.0,
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rope_scaling=None,
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classifier_dropout=None,
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pack_qkv=True,
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unpad_inputs=False,
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use_memory_efficient_attention=False,
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logn_attention_scale=False,
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logn_attention_clip1=False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_type = layer_norm_type
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.classifier_dropout = classifier_dropout
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self.pack_qkv = pack_qkv
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self.unpad_inputs = unpad_inputs
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self.use_memory_efficient_attention = use_memory_efficient_attention
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self.logn_attention_scale = logn_attention_scale
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self.logn_attention_clip1 = logn_attention_clip1
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Binary file not shown.
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
|
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
|
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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File diff suppressed because it is too large
Load Diff
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{
|
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"added_tokens_decoder": {
|
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"0": {
|
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"content": "[PAD]",
|
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
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},
|
||||
"100": {
|
||||
"content": "[UNK]",
|
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"lstrip": false,
|
||||
"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
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},
|
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"101": {
|
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"content": "[CLS]",
|
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
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},
|
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"102": {
|
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"content": "[SEP]",
|
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"lstrip": false,
|
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"normalized": false,
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"rstrip": false,
|
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"single_word": false,
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"special": true
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},
|
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"103": {
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"content": "[MASK]",
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"lstrip": false,
|
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"normalized": false,
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
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}
|
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},
|
||||
"clean_up_tokenization_spaces": true,
|
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"cls_token": "[CLS]",
|
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"do_lower_case": true,
|
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"eos_token": "[CLS]",
|
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"mask_token": "[MASK]",
|
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"max_length": 8000,
|
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"model_max_length": 32768,
|
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"pad_to_multiple_of": null,
|
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"pad_token": "[PAD]",
|
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"pad_token_type_id": 0,
|
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
|
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"strip_accents": null,
|
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"tokenize_chinese_chars": true,
|
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"tokenizer_class": "BertTokenizer",
|
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"truncation_side": "right",
|
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"truncation_strategy": "longest_first",
|
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"unk_token": "[UNK]"
|
||||
}
|
||||
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Reference in New Issue