Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc")
model = AutoModelForSequenceClassification.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc", device_map="auto")Quick Links
distilbert-base-uncased-distilled-clinc
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: 0.0471
- Accuracy: 0.9319
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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 9
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 0.2645 | 0.6329 |
| 0.3989 | 2.0 | 636 | 0.1324 | 0.8510 |
| 0.3989 | 3.0 | 954 | 0.0897 | 0.8984 |
| 0.1467 | 4.0 | 1272 | 0.0702 | 0.9084 |
| 0.0939 | 5.0 | 1590 | 0.0591 | 0.9245 |
| 0.0939 | 6.0 | 1908 | 0.0531 | 0.9265 |
| 0.0749 | 7.0 | 2226 | 0.0499 | 0.9306 |
| 0.0665 | 8.0 | 2544 | 0.0477 | 0.9316 |
| 0.0665 | 9.0 | 2862 | 0.0471 | 0.9319 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
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Model tree for KCourtney/distilbert-base-uncased-distilled-clinc
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KCourtney/distilbert-base-uncased-distilled-clinc")