Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use adriansanz/modelofine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriansanz/modelofine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adriansanz/modelofine")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adriansanz/modelofine") model = AutoModelForSequenceClassification.from_pretrained("adriansanz/modelofine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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base_model: projecte-aina/roberta-base-ca-v2-cased-te
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tags:
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- generated_from_trainer
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model-index:
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- name: modelofine
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results: []
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# modelofine
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This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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## Model description
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base_model: projecte-aina/roberta-base-ca-v2-cased-te
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: modelofine
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results: []
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# modelofine
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This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.8036
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- Accuracy: 0.0672
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## Model description
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