Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slickdata/finetuned-Sentiment-classfication-ROBERTA-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model") model = AutoModelForSequenceClassification.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files
config.json
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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.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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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": "4.31.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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pytorch_model.bin
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runs/Jul23_02-07-14_8e5bf2f6b407/events.out.tfevents.1690078161.8e5bf2f6b407.373.0
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training_args.bin
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size 4219
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