---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
- f1
model-index:
- name: EraClassifierBiLSTM
  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. -->

# EraClassifierBiLSTM

This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1056
- Accuracy: 0.5736
- F1: 0.4174

## 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: 4.761974698772928e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: reduce_lr_on_plateau
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 1.2797        | 0.1031 | 2000  | 1.3522          | 0.4608   | 0.2486 |
| 1.1521        | 0.2063 | 4000  | 1.2422          | 0.4987   | 0.3139 |
| 1.0887        | 0.3094 | 6000  | 1.2189          | 0.5056   | 0.3223 |
| 1.0432        | 0.4126 | 8000  | 1.1715          | 0.5252   | 0.3479 |
| 1.019         | 0.5157 | 10000 | 1.2021          | 0.5150   | 0.3304 |
| 0.9963        | 0.6188 | 12000 | 1.1789          | 0.5252   | 0.3487 |
| 0.976         | 0.7220 | 14000 | 1.1151          | 0.5759   | 0.3983 |
| 0.9544        | 0.8251 | 16000 | 1.1800          | 0.5299   | 0.3529 |
| 0.9455        | 0.9283 | 18000 | 1.1866          | 0.5415   | 0.3662 |
| 0.9276        | 1.0314 | 20000 | 1.1744          | 0.5350   | 0.3792 |
| 0.9167        | 1.1345 | 22000 | 1.1032          | 0.5774   | 0.4120 |
| 0.9084        | 1.2377 | 24000 | 1.1312          | 0.5553   | 0.3818 |
| 0.8758        | 1.3408 | 26000 | 1.1042          | 0.5667   | 0.4109 |
| 0.859         | 1.4440 | 28000 | 1.1065          | 0.5733   | 0.4125 |
| 0.8607        | 1.5471 | 30000 | 1.1104          | 0.5695   | 0.4115 |
| 0.8526        | 1.6503 | 32000 | 1.1011          | 0.5830   | 0.4255 |
| 0.8559        | 1.7534 | 34000 | 1.1083          | 0.5765   | 0.4136 |
| 0.8501        | 1.8565 | 36000 | 1.1113          | 0.5752   | 0.4163 |
| 0.8497        | 1.9597 | 38000 | 1.0935          | 0.5775   | 0.4220 |
| 0.8473        | 2.0628 | 40000 | 1.1092          | 0.5745   | 0.4181 |
| 0.8441        | 2.1660 | 42000 | 1.1095          | 0.5733   | 0.4164 |
| 0.8396        | 2.2691 | 44000 | 1.0935          | 0.5852   | 0.4299 |
| 0.8391        | 2.3722 | 46000 | 1.1054          | 0.5744   | 0.4160 |
| 0.8401        | 2.4754 | 48000 | 1.1008          | 0.5755   | 0.4198 |
| 0.8327        | 2.5785 | 50000 | 1.1097          | 0.5712   | 0.4132 |
| 0.838         | 2.6817 | 52000 | 1.1055          | 0.5720   | 0.4143 |
| 0.8329        | 2.7848 | 54000 | 1.1055          | 0.5728   | 0.4165 |
| 0.8346        | 2.8879 | 56000 | 1.1038          | 0.5743   | 0.4172 |
| 0.8353        | 2.9911 | 58000 | 1.1090          | 0.5728   | 0.4167 |
| 0.8385        | 3.0942 | 60000 | 1.1013          | 0.5755   | 0.4201 |
| 0.8337        | 3.1974 | 62000 | 1.1088          | 0.5733   | 0.4163 |
| 0.8256        | 3.3005 | 64000 | 1.1076          | 0.5748   | 0.4177 |
| 0.8367        | 3.4036 | 66000 | 1.1066          | 0.5730   | 0.4159 |
| 0.831         | 3.5068 | 68000 | 1.1083          | 0.5732   | 0.4164 |
| 0.8283        | 3.6099 | 70000 | 1.1067          | 0.5744   | 0.4173 |
| 0.8349        | 3.7131 | 72000 | 1.1058          | 0.5747   | 0.4180 |
| 0.8313        | 3.8162 | 74000 | 1.1058          | 0.5741   | 0.4171 |
| 0.8313        | 3.9193 | 76000 | 1.1065          | 0.5735   | 0.4169 |
| 0.8309        | 4.0225 | 78000 | 1.1067          | 0.5736   | 0.4171 |
| 0.8331        | 4.1256 | 80000 | 1.1055          | 0.5744   | 0.4174 |
| 0.8371        | 4.2288 | 82000 | 1.1058          | 0.5735   | 0.4167 |
| 0.8344        | 4.3319 | 84000 | 1.1060          | 0.5734   | 0.4166 |
| 0.8291        | 4.4350 | 86000 | 1.1049          | 0.5747   | 0.4185 |
| 0.8343        | 4.5382 | 88000 | 1.1053          | 0.5735   | 0.4171 |
| 0.8293        | 4.6413 | 90000 | 1.1056          | 0.5736   | 0.4174 |
| 0.8294        | 4.7445 | 92000 | 1.1056          | 0.5736   | 0.4174 |
| 0.8316        | 4.8476 | 94000 | 1.1055          | 0.5736   | 0.4174 |
| 0.8264        | 4.9508 | 96000 | 1.1056          | 0.5736   | 0.4174 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0