Instructions to use CeroShrijver/m3e-base-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CeroShrijver/m3e-base-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CeroShrijver/m3e-base-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CeroShrijver/m3e-base-text-classification") model = AutoModelForSequenceClassification.from_pretrained("CeroShrijver/m3e-base-text-classification") - Notebooks
- Google Colab
- Kaggle
m3e-base-text-classification
This model is a fine-tuned version of moka-ai/m3e-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6529
- Accuracy: 0.7826
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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.495 | 1.0 | 1009 | 0.5175 | 0.7783 |
| 0.3792 | 2.0 | 2018 | 0.5600 | 0.7748 |
| 0.2503 | 3.0 | 3027 | 0.6529 | 0.7826 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.6
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