Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("71-vueT10/chess-guesser-distilbert")
model = AutoModelForSequenceClassification.from_pretrained("71-vueT10/chess-guesser-distilbert")Quick Links
chess-guesser-distilbert
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: 0.6500
Model description
More information needed
Intended uses & limitations
Can be used for elo guessing.
Training and evaluation data
Updated this model to include 50,000 games. 25,000 games for Bucket 1(400-1000) and 25,000 games for Bucket 2(1001-1200). 90% training and 10% validation array. Previously had 2,000 games each.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6799 | 1.0 | 1407 | 0.6864 |
| 0.6598 | 2.0 | 2814 | 0.6590 |
| 0.6445 | 3.0 | 4221 | 0.6500 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for 71-vueT10/chess-guesser-distilbert
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="71-vueT10/chess-guesser-distilbert")