Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use stillett/grader_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stillett/grader_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stillett/grader_model_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stillett/grader_model_1") model = AutoModelForSequenceClassification.from_pretrained("stillett/grader_model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
grader_model_1
This model is a fine-tuned version of stillett/grader_model_1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9553
- F1: 0.5912
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: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.8929 | 1.0 | 563 | 0.9337 | 0.5879 |
| 0.8384 | 2.0 | 1126 | 0.9346 | 0.5873 |
| 0.7975 | 3.0 | 1689 | 0.9309 | 0.6004 |
| 0.764 | 4.0 | 2252 | 0.9408 | 0.5963 |
| 0.732 | 5.0 | 2815 | 0.9483 | 0.5948 |
| 0.7026 | 6.0 | 3378 | 0.9553 | 0.5912 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
- 3
Model tree for stillett/grader_model_1
Unable to build the model tree, the base model loops to the model itself. Learn more.