Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("HanBi/my_awesome_model")
model = AutoModelForSequenceClassification.from_pretrained("HanBi/my_awesome_model")Quick Links
my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on the movie_rationales dataset. It achieves the following results on the evaluation set:
- Loss: 0.2762
- Accuracy: 0.8844
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 100 | 0.4182 | 0.8040 |
| No log | 2.0 | 200 | 0.2762 | 0.8844 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
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Dataset used to train HanBi/my_awesome_model
Evaluation results
- Accuracy on movie_rationalestest set self-reported0.884
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HanBi/my_awesome_model")