Commit From AutoTrain
Browse files- .gitattributes +2 -0
- README.md +45 -0
- config.json +47 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
- vocab.txt +0 -0
.gitattributes
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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tags: autotrain
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language: en
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widget:
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- text: "I love AutoTrain 🤗"
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datasets:
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- Shenzy2/autotrain-data-tk
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co2_eq_emissions: 0.004663044473485149
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---
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# Model Trained Using AutoTrain
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- Problem type: Entity Extraction
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- Model ID: 1181244086
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- CO2 Emissions (in grams): 0.004663044473485149
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## Validation Metrics
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- Loss: 0.5532978773117065
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- Accuracy: 0.8263097949886105
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- Precision: 0.5104166666666666
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- Recall: 0.4681528662420382
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- F1: 0.4883720930232558
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## Usage
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You can use cURL to access this model:
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```
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$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Shenzy2/autotrain-tk-1181244086
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```
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Or Python API:
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```
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from transformers import AutoModelForTokenClassification, AutoTokenizer
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model = AutoModelForTokenClassification.from_pretrained("Shenzy2/autotrain-tk-1181244086", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("Shenzy2/autotrain-tk-1181244086", use_auth_token=True)
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inputs = tokenizer("I love AutoTrain", return_tensors="pt")
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outputs = model(**inputs)
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```
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config.json
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{
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"_name_or_path": "AutoTrain",
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"_num_labels": 7,
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"architectures": [
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"BertForTokenClassification"
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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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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "B-Nolabel",
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"1": "B-design_concept",
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"2": "B-ui_element",
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"3": "I-Nolabel",
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"4": "I-design_concept",
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"5": "I-ui_element",
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"6": "O"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-Nolabel": 0,
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"B-design_concept": 1,
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"B-ui_element": 2,
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"I-Nolabel": 3,
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"I-design_concept": 4,
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"I-ui_element": 5,
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"O": 6
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},
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"layer_norm_eps": 1e-12,
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"max_length": 96,
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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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"padding": "max_length",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.20.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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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 sha256:8caf5ae6ff0b8a703fb7e9d87e83f7f90702feebc7007cdb1cc791ed935b9e60
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size 430970673
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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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"cls_token": "[CLS]",
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"do_lower_case": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "AutoTrain",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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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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