Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jayavibhav/distilbert-classification-10ksamples")
model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/distilbert-classification-10ksamples")Quick Links
distilbert-classification-10ksamples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1977
- Accuracy: 0.96
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 |
|---|---|---|---|---|
| 0.1853 | 1.0 | 625 | 0.1682 | 0.9577 |
| 0.0436 | 2.0 | 1250 | 0.1977 | 0.96 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for jayavibhav/distilbert-classification-10ksamples
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/distilbert-classification-10ksamples")