Adding ONNX file of this model
Browse filesBeep boop I am the [ONNX export bot 🤖🏎️](https://huggingface.co/spaces/onnx/export). On behalf of [zumoko](https://huggingface.co/zumoko), I would like to add to this repository the model converted to ONNX.
What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at [onnx.ai](https://onnx.ai/)!
The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with 🤗 Optimum through ONNX Runtime, check out how [here](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models)!
- README.md +4 -0
- onnx/config.json +26 -0
- onnx/model.onnx +3 -0
- onnx/special_tokens_map.json +7 -0
- onnx/tokenizer.json +0 -0
- onnx/tokenizer_config.json +58 -0
- onnx/vocab.txt +0 -0
README.md
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TinyBERT: Distilling BERT for Natural Language Understanding
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========
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TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
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---
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tags:
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- onnx
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---
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TinyBERT: Distilling BERT for Natural Language Understanding
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========
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TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
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onnx/config.json
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{
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"_attn_implementation_autoset": true,
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"attention_probs_dropout_prob": 0.1,
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"cell": {},
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"classifier_dropout": null,
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"emb_size": 312,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 312,
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"initializer_range": 0.02,
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"intermediate_size": 1200,
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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": 4,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"pre_trained": "",
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"structure": [],
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"torch_dtype": "float32",
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"transformers_version": "4.51.3",
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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 sha256:15e5dab447a8e25ecbbc1151b0b982ecc7d1b803fccb461675dff75f3dad5299
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size 57109598
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onnx/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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onnx/tokenizer.json
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onnx/tokenizer_config.json
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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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},
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"100": {
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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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"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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},
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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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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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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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onnx/vocab.txt
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