Commit From AutoNLP
Browse files- .gitattributes +2 -0
- README.md +52 -0
- config.json +42 -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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*.tar.gz 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: autonlp
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language: en
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widget:
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- text: "I love AutoNLP 🤗"
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datasets:
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- Crasher222/autonlp-data-kaggle-test
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co2_eq_emissions: 60.744727079482495
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---
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# Model Trained Using AutoNLP
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- Problem type: Multi-class Classification
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- Model ID: 25805800
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- CO2 Emissions (in grams): 60.744727079482495
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## Validation Metrics
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- Loss: 0.4422711133956909
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- Accuracy: 0.8615328555811976
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- Macro F1: 0.8642434650461513
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- Micro F1: 0.8615328555811976
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- Weighted F1: 0.8617743626671308
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- Macro Precision: 0.8649112225076049
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- Micro Precision: 0.8615328555811976
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- Weighted Precision: 0.8625407179375096
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- Macro Recall: 0.8640777539828228
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- Micro Recall: 0.8615328555811976
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- Weighted Recall: 0.8615328555811976
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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 AutoNLP"}' https://api-inference.huggingface.co/models/Crasher222/autonlp-kaggle-test-25805800
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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("Crasher222/autonlp-kaggle-test-25805800", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("Crasher222/autonlp-kaggle-test-25805800", use_auth_token=True)
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inputs = tokenizer("I love AutoNLP", 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": "AutoNLP",
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"_num_labels": 5,
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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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": 1024,
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"id2label": {
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"0": "0",
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"1": "1",
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"2": "2",
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"3": "3",
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"4": "4"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"0": 0,
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"1": 1,
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"2": 2,
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"3": 3,
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"4": 4
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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": 16,
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"num_hidden_layers": 24,
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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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"problem_type": "single_label_classification",
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"transformers_version": "4.8.0",
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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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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d52c9fc5d620938b6a4d1a046ea6c9140300d23c8989549448de99ca39239f15
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size 1340749741
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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:e1506f7bde1c09f6e582eba812db9aa7aedb09009f33f9d4a389992ea4df90cd
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size 3616
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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": "AutoNLP", "tokenizer_class": "BertTokenizer"}
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vocab.txt
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