Commit From AutoTrain
Browse files- .gitattributes +3 -0
- README.md +46 -0
- config.json +36 -0
- pytorch_model.bin +3 -0
- sample_input.pkl +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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*.pkl 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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- deepesh0x/autotrain-data-mlsec
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co2_eq_emissions: 33.183779535405364
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---
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# Model Trained Using AutoTrain
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- Problem type: Binary Classification
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- Model ID: 1013333726
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- CO2 Emissions (in grams): 33.183779535405364
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## Validation Metrics
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- Loss: 0.1998898833990097
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- Accuracy: 0.9226923076923077
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- Precision: 0.9269808389435525
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- Recall: 0.9177134068187645
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- AUC: 0.9785380985232148
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- F1: 0.9223238438747907
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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/deepesh0x/autotrain-mlsec-1013333726
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```
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Or Python API:
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```
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", 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": 2,
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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": "negative",
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"1": "positive"
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},
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"initializer_range": 0.02,
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"label2id": {
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"negative": 0,
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"positive": 1
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},
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"max_length": 512,
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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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"padding": "max_length",
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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": "4.15.0",
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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 sha256:3f2944850709d05f172c6782978960c85bd6a859c0147fc9747da0c85597a6d8
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size 267860081
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sample_input.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:30aa7c9c59e6b1de09c3b6e0029a400c9cf66eae1d48c3d30fd2a78c0f471313
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size 9206
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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": "AutoTrain", "tokenizer_class": "DistilBertTokenizer"}
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vocab.txt
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