Text Classification
Transformers
Safetensors
mistral
Generated from Trainer
text-embeddings-inference
Instructions to use shirleylqs/mistral-snomed-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shirleylqs/mistral-snomed-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shirleylqs/mistral-snomed-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shirleylqs/mistral-snomed-classification") model = AutoModelForSequenceClassification.from_pretrained("shirleylqs/mistral-snomed-classification") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4972
- Accuracy: 0.9440
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 4
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3355 | 1.0 | 7990 | 0.6075 | 0.9138 |
| 0.0 | 2.0 | 15980 | 0.4972 | 0.9440 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for shirleylqs/mistral-snomed-classification
Base model
mistralai/Mistral-7B-v0.1