Feature Extraction
Transformers.js
ONNX
English
bert
decidekit
system-one
text-classification
zero-shot-classification
calibration
on-device
Instructions to use debuffjp/decide-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use debuffjp/decide-small with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'debuffjp/decide-small');
Copy of Xenova/bge-small-en-v1.5@ea104da
Browse files- config.json +30 -0
- onnx/model.onnx +3 -0
- onnx/model_fp16.onnx +3 -0
- onnx/model_quantized.onnx +3 -0
- quantize_config.json +30 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "BAAI/bge-small-en-v1.5",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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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": 384,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.34.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:828e1496d7fabb79cfa4dcd84fa38625c0d3d21da474a00f08db0f559940cf35
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size 133093490
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onnx/model_fp16.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:4d76223b73a3fd708f009a93a830a6ed5e536b07c77d3c812ba7b35a4988d767
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size 66749212
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onnx/model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:6c9c6101a956d62dfb5e7190c538226c0c5bb9cb27b651234b6df063ee7dbfe4
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size 34014426
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quantize_config.json
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{
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"per_channel": true,
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"reduce_range": true,
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"per_model_config": {
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"model": {
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"op_types": [
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"Shape",
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"Softmax",
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"Transpose",
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"Erf",
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"Reshape",
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"Mul",
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"Concat",
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"ReduceMean",
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"Cast",
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"Div",
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"MatMul",
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"Unsqueeze",
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"Constant",
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"Add",
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"Sqrt",
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"Gather",
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"Slice",
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"Pow",
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"Sub"
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],
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"weight_type": "QInt8"
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}
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}
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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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_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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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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"unk_token": "[UNK]"
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}
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vocab.txt
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