Instructions to use rajendrabaskota/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajendrabaskota/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajendrabaskota/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajendrabaskota/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("rajendrabaskota/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): ca8b397
rajendrabaskota/hc3-wiki-domain-classification-roberta
Browse files- README.md +2 -2
- config.json +1 -1
- training_args.bin +1 -1
README.md
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---
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license: mit
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base_model: rajendrabaskota/
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tags:
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- generated_from_trainer
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model-index:
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# tmp_trainer
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This model is a fine-tuned version of [rajendrabaskota/
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## Model description
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---
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license: mit
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base_model: rajendrabaskota/hc3-wiki-domain-classification-roberta
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tags:
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- generated_from_trainer
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model-index:
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# tmp_trainer
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This model is a fine-tuned version of [rajendrabaskota/hc3-wiki-domain-classification-roberta](https://huggingface.co/rajendrabaskota/hc3-wiki-domain-classification-roberta) on an unknown dataset.
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## Model description
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config.json
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{
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"_name_or_path": "rajendrabaskota/
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"architectures": [
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"RobertaForSequenceClassification"
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],
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{
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"_name_or_path": "rajendrabaskota/hc3-wiki-domain-classification-roberta",
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"architectures": [
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"RobertaForSequenceClassification"
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],
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training_args.bin
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size 4027
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