Text Classification
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/tiny-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/tiny-mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/tiny-mistral")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/tiny-mistral") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/tiny-mistral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- name: tiny-mistral
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results: []
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## Metrics
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- loss: 2.4489
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- accuracy: 0.7250
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# tiny-mistral
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This model is a fine-tuned version of [openaccess-ai-collective/tiny-mistral](https://huggingface.co/openaccess-ai-collective/tiny-mistral) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.5607
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- Accuracy: 0.7126
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- Precision: 0.7033
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- name: tiny-mistral
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results: []
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---
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## Metrics Upon Eval with max_length = 512
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- loss: 2.4489
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- accuracy: 0.7250
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# tiny-mistral
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This model is a fine-tuned version of [openaccess-ai-collective/tiny-mistral](https://huggingface.co/openaccess-ai-collective/tiny-mistral) on an unknown dataset.
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It achieves the following results on the evaluation set (at last epoch):
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- Loss: 2.5607
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- Accuracy: 0.7126
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- Precision: 0.7033
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