Token Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use shubhanmathur/MLMA_Lab_8_GPT_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhanmathur/MLMA_Lab_8_GPT_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shubhanmathur/MLMA_Lab_8_GPT_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shubhanmathur/MLMA_Lab_8_GPT_model") model = AutoModelForTokenClassification.from_pretrained("shubhanmathur/MLMA_Lab_8_GPT_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MLMA_Lab_8_GPT_model
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1509
- Precision: 0.4333
- Recall: 0.5197
- F1: 0.4726
- Accuracy: 0.9564
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3275 | 1.0 | 679 | 0.1747 | 0.2975 | 0.4460 | 0.3569 | 0.9449 |
| 0.169 | 2.0 | 1358 | 0.1661 | 0.3892 | 0.4956 | 0.4360 | 0.9510 |
| 0.0994 | 3.0 | 2037 | 0.1509 | 0.4333 | 0.5197 | 0.4726 | 0.9564 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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