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license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: TTC4900Model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TTC4900Model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1884
- Accuracy: 0.6272
- F1: 0.7392
- Precision: 0.7048
- Recall: 0.8129
## 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-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5316 | 0.56 | 50 | 1.1986 | 0.6262 | 0.4825 | 0.5074 | 0.5748 |
| 0.5421 | 1.12 | 100 | 0.2282 | 0.9464 | 0.9318 | 0.9579 | 0.9159 |
| 0.1327 | 1.69 | 150 | 0.2318 | 0.9499 | 0.9542 | 0.9479 | 0.9637 |
| 0.1214 | 2.25 | 200 | 0.1772 | 0.9669 | 0.9688 | 0.9652 | 0.9730 |
| 0.0632 | 2.81 | 250 | 0.2155 | 0.9669 | 0.9688 | 0.9681 | 0.9696 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|