iam-tsr/feedback-classifier
By iam-tsr · ↓ 0 · ♥ 0 · Updated 2026-02-28
What's in this repo
This page lists the files in iam-tsr/feedback-classifier, including model.onnx, model_qint8.onnx.
- 2 ONNX models (.onnx files)
- 12 files, including 5 .json, 2 .onnx, 1 .md, 1 .joblib, 1 .safetensors, 1 .txt
- Last updated 2026-02-28
ONNX files
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
results
This model is a fine-tuned version of distilbert/distilbert-base-uncased on employ feedback dataset. It achieves the following results on the evaluation set:
- Loss: 0.1520
- Accuracy: 0.9423
- Precision: 0.9181
- Recall: 0.9284
- F1: 0.9228
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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.1735 | 1.0 | 172 | 0.1631 | 0.9462 | 0.9249 | 0.9297 | 0.9272 |
| 0.1824 | 2.0 | 344 | 0.1619 | 0.9385 | 0.9108 | 0.9308 | 0.9191 |
| 0.1555 | 3.0 | 516 | 0.1520 | 0.9423 | 0.9181 | 0.9284 | 0.9228 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.3.0
- Tokenizers 0.22.2
Model info
Created: 2026-01-09
Updated: 2026-02-28
View on Hugging Face