Commit From AutoNLP
Browse files- .gitattributes +2 -0
- README.md +52 -0
- config.json +44 -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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*tfevents* filter=lfs diff=lfs merge=lfs -text
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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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- juliensimon/autonlp-data-song-lyrics
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co2_eq_emissions: 112.75546781635975
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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: 18753417
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- CO2 Emissions (in grams): 112.75546781635975
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## Validation Metrics
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- Loss: 0.9065971970558167
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- Accuracy: 0.6680274633512711
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- Macro F1: 0.5384854358272774
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- Micro F1: 0.6680274633512711
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- Weighted F1: 0.6414749238882866
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- Macro Precision: 0.6744495173269196
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- Micro Precision: 0.6680274633512711
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- Weighted Precision: 0.6634090047492259
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- Macro Recall: 0.5078466493896978
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- Micro Recall: 0.6680274633512711
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- Weighted Recall: 0.6680274633512711
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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/juliensimon/autonlp-song-lyrics-18753417
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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("juliensimon/autonlp-song-lyrics-18753417", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-song-lyrics-18753417", 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": 6,
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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": 768,
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"id2label": {
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"0": "Dance",
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"1": "Heavy Metal",
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"2": "Hip Hop",
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"3": "Indie",
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"4": "Pop",
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"5": "Rock"
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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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"Dance": 0,
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"Heavy Metal": 1,
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"Hip Hop": 2,
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"Indie": 3,
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"Pop": 4,
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"Rock": 5
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},
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"layer_norm_eps": 1e-12,
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"max_length": 512,
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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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"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 sha256:449648ac218faffa0c91c3c0e3dee489ecf4b08681014301c49e528f3048c060
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size 438031533
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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:cc598520e1d49664d68dad19709ed2df9d82447bd32421bd28485839882ab33f
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size 13606
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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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