Text Classification
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distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wsyar/llmhw01")
model = AutoModelForSequenceClassification.from_pretrained("wsyar/llmhw01")Quick Links
llmhw01
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: 0.8872
- Matthews Correlation: 0.5406
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: 2.1796366105472604e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 3
- 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.518 | 1.0 | 535 | 0.4573 | 0.4819 |
| 0.3464 | 2.0 | 1070 | 0.5350 | 0.4747 |
| 0.2281 | 3.0 | 1605 | 0.6534 | 0.5211 |
| 0.1642 | 4.0 | 2140 | 0.7936 | 0.5472 |
| 0.1181 | 5.0 | 2675 | 0.8872 | 0.5406 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Model tree for wsyar/llmhw01
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wsyar/llmhw01")