Commit From AutoTrain
Browse files- .gitattributes +3 -0
- README.md +52 -0
- config.json +85 -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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*.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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*.bin.* 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: autotrain
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language: ar
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widget:
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- text: "I love AutoTrain 🤗"
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datasets:
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- zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412
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co2_eq_emissions: 0.06170374019107819
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---
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# Model Trained Using AutoTrain
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- Problem type: Multi-class Classification
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- Model ID: 920730227
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- CO2 Emissions (in grams): 0.06170374019107819
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## Validation Metrics
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- Loss: 0.5905918478965759
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- Accuracy: 0.8687837028160575
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- Macro F1: 0.7777187122151491
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- Micro F1: 0.8687837028160575
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- Weighted F1: 0.8673230166815299
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- Macro Precision: 0.796117563625016
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- Micro Precision: 0.8687837028160575
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- Weighted Precision: 0.8692944353097692
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- Macro Recall: 0.7732013751753718
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- Micro Recall: 0.8687837028160575
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- Weighted Recall: 0.8687837028160575
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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/zenkri/autotrain-Arabic_Poetry_by_Subject-920730227
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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("zenkri/autotrain-Arabic_Poetry_by_Subject-920730227", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("zenkri/autotrain-Arabic_Poetry_by_Subject-920730227", 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": 25,
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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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"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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"12": "\u0633\u064a\u0627\u0633\u064a\u0629",
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"13": "\u0634\u0648\u0642",
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"14": "\u0635\u0628\u0631",
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"15": "\u0639\u0627\u0645\u0647",
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"19": "\u0641\u0631\u0627\u0642",
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"20": "\u0642\u0635\u064a\u0631\u0647",
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"21": "\u0645\u062f\u062d",
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"22": "\u0646\u0635\u064a\u062d\u0629",
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"23": "\u0647\u062c\u0627\u0621",
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"24": "\u0648\u0637\u0646\u064a\u0647"
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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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"\u0648\u0637\u0646\u064a\u0647": 24
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},
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"layer_norm_eps": 1e-12,
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"max_length": 64,
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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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"output_past": true,
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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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"torch_dtype": "float32",
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"transformers_version": "4.15.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32000
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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:e6c57e30a3bb6a79081f724b4bbc846de6d190aef27823878ee9f820fef8e255
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size 442630381
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sample_input.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:734356c306b4018302eb2904640755c58c7a8f0d22ea34a909bba550fd9ed542
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size 2848
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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, "special_tokens_map_file": null, "full_tokenizer_file": null, "name_or_path": "AutoTrain", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
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vocab.txt
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