Instructions to use Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model") model = AutoModelForSequenceClassification.from_pretrained("Joshi-Aryan/Fine_Tuned_HF_Language_Identification_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
ecb54cd
1
Parent(s): b85ac13
Delete checkpoint-2000
Browse files- checkpoint-2000/config.json +0 -43
- checkpoint-2000/optimizer.pt +0 -3
- checkpoint-2000/pytorch_model.bin +0 -3
- checkpoint-2000/rng_state.pth +0 -3
- checkpoint-2000/scaler.pt +0 -3
- checkpoint-2000/scheduler.pt +0 -3
- checkpoint-2000/sentencepiece.bpe.model +0 -3
- checkpoint-2000/special_tokens_map.json +0 -15
- checkpoint-2000/tokenizer.json +0 -3
- checkpoint-2000/tokenizer_config.json +0 -19
- checkpoint-2000/trainer_state.json +0 -15
- checkpoint-2000/training_args.bin +0 -3
checkpoint-2000/config.json
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{
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"_name_or_path": "xlm-roberta-base",
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"architectures": [
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"XLMRobertaForSequenceClassification"
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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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"classifier_dropout": null,
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"eos_token_id": 2,
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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": "arabic",
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"1": "english",
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"2": "french",
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"3": "german",
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"4": "russian"
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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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"arabic": 0,
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"english": 1,
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"french": 2,
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"german": 3,
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"russian": 4
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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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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"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": "4.28.0",
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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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checkpoint-2000/optimizer.pt
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checkpoint-2000/pytorch_model.bin
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checkpoint-2000/sentencepiece.bpe.model
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checkpoint-2000/special_tokens_map.json
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checkpoint-2000/tokenizer.json
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checkpoint-2000/tokenizer_config.json
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checkpoint-2000/trainer_state.json
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checkpoint-2000/training_args.bin
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