Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("anth0nyhak1m/CFGFP_ProductGroupCalssifier_v1")
model = AutoModelForSequenceClassification.from_pretrained("anth0nyhak1m/CFGFP_ProductGroupCalssifier_v1")Quick Links
CFGFP_ProductGroupCalssifier_v1
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1960
- Accuracy: 0.9644
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2342 | 1.0 | 3804 | 0.1990 | 0.9464 |
| 0.1457 | 2.0 | 7608 | 0.1844 | 0.9567 |
| 0.1083 | 3.0 | 11412 | 0.1864 | 0.9602 |
| 0.0675 | 4.0 | 15216 | 0.1943 | 0.9641 |
| 0.0464 | 5.0 | 19020 | 0.1960 | 0.9644 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anth0nyhak1m/CFGFP_ProductGroupCalssifier_v1")