Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use deathperminutV2/NLP_sequence_clasiffication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deathperminutV2/NLP_sequence_clasiffication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deathperminutV2/NLP_sequence_clasiffication")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deathperminutV2/NLP_sequence_clasiffication") model = AutoModelForSequenceClassification.from_pretrained("deathperminutV2/NLP_sequence_clasiffication") - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- glue
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# NLP_sequence_clasiffication
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue
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It achieves the following results on the evaluation set:
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- Loss: 0.5325
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- Accuracy: 0.8505
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---
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license: apache-2.0
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tags:
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- text-classification
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- generated_from_trainer
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datasets:
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- glue
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# NLP_sequence_clasiffication
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.5325
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- Accuracy: 0.8505
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