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Browse files- README.md +39 -0
- config.json +35 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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language: en
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tags:
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- exbert
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license: apache-2.0
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datasets:
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- Confidential
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---
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# BERT base model (uncased)
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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## Model description
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BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
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was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
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was pretrained with two objectives:
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- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
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the entire masked sentence through the model and has to predict the masked words. This is different from traditional
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recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
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GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
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sentence.
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- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
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they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
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predict if the two sentences were following each other or not.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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## Model description [sbcBI/sentiment_analysis]
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This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for
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further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.
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config.json
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2"
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},
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"initializer_range": 0.02,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.18.0",
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bd95828637be19dbfa1a4b038014665cb809fa7fddcf8b53f5e3daf01aa84bf
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size 134
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased", "tokenizer_class": "DistilBertTokenizer"}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:6b6d901a867ea6c03426654543f3de1d2d9fdea9ec8c2a3d6c7abb442f3f9ee7
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size 129
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vocab.txt
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