Commit From AutoNLP
Browse files- .gitattributes +2 -0
- README.md +60 -0
- config.json +31 -0
- pytorch_model.bin +3 -0
- sample_input.pkl +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -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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*.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:
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- autonlp
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- question-answering
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language: unk
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widget:
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- text: "Who loves AutoNLP?"
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context: "Everyone loves AutoNLP"
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datasets:
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- teacookies/autonlp-data-roberta-base-squad2
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co2_eq_emissions: 54.44076291568145
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---
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# Model Trained Using AutoNLP
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- Problem type: Extractive Question Answering
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- Model ID: 24465514
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- CO2 Emissions (in grams): 54.44076291568145
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## Validation Metrics
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- Loss: 0.5786784887313843
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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 '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-roberta-base-squad2-24465514
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```
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Or Python API:
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```
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import torch
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer
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model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465514", use_auth_token=True)
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tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-roberta-base-squad2-24465514", use_auth_token=True)
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from transformers import BertTokenizer, BertForQuestionAnswering
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question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
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inputs = tokenizer(question, text, return_tensors='pt')
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start_positions = torch.tensor([1])
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end_positions = torch.tensor([3])
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outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
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loss = outputs.loss
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start_scores = outputs.start_logits
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end_scores = outputs.end_logits
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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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"architectures": [
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"XLMRobertaForQuestionAnswering"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 2,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"language": "english",
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"layer_norm_eps": 1e-05,
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"max_length": 384,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"name": "XLMRoberta",
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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": 1,
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"padding": "max_length",
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"position_embedding_type": "absolute",
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"transformers_version": "4.8.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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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 sha256:2d5d8197a0d318eeec9bcdb4c2f1f6a9513fa04701473681ce111e5e0bd9486e
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size 1109903089
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sample_input.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:3c10ad1d5cca1db1e49fd1a95b16ac91723c89f3099049ece8fbb508d9fef4e0
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size 2015
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": "<mask>"}
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tokenizer.json
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tokenizer_config.json
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{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "do_lower_case": false, "model_max_length": 512, "full_tokenizer_file": null, "special_tokens_map_file": "/root/.cache/huggingface/transformers/b285d730ad6193f7e96b41152fbc5fb05620f0961f6f513e2d36868dfb1eb2a7.0dc5b1041f62041ebbd23b1297f2f573769d5c97d8b7c28180ec86b8f6185aa8", "name_or_path": "AutoNLP", "sp_model_kwargs": {}, "tokenizer_class": "XLMRobertaTokenizer"}
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