Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use pabagcha/roberta_crypto_profiling_task1_complete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabagcha/roberta_crypto_profiling_task1_complete with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pabagcha/roberta_crypto_profiling_task1_complete")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pabagcha/roberta_crypto_profiling_task1_complete") model = AutoModelForSequenceClassification.from_pretrained("pabagcha/roberta_crypto_profiling_task1_complete") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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- F1: 0.
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## Model description
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7421
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- Accuracy: 0.6954
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- F1: 0.7128
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## Model description
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