---
license: mit
base_model: allistair99/tinybert-6l-768d-BiLSTM-finetuned-squad-optiparam
tags:
- generated_from_trainer
model-index:
- name: tinybert-base-uncased-BiLSTM-Optiparam-ADVQA36K-V1
  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. -->

# tinybert-base-uncased-BiLSTM-Optiparam-ADVQA36K-V1

This model is a fine-tuned version of [allistair99/tinybert-6l-768d-BiLSTM-finetuned-squad-optiparam](https://huggingface.co/allistair99/tinybert-6l-768d-BiLSTM-finetuned-squad-optiparam) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9656

## 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 60
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0036        | 0.0599 | 100  | 2.5920          |
| 2.8317        | 0.1198 | 200  | 2.5140          |
| 2.7926        | 0.1796 | 300  | 2.5142          |
| 2.7339        | 0.2395 | 400  | 2.4734          |
| 2.7291        | 0.2994 | 500  | 2.4903          |
| 2.7906        | 0.3593 | 600  | 2.4795          |
| 2.641         | 0.4192 | 700  | 2.4862          |
| 2.5682        | 0.4790 | 800  | 2.5682          |
| 2.6871        | 0.5389 | 900  | 2.4674          |
| 2.7482        | 0.5988 | 1000 | 2.4033          |
| 2.6894        | 0.6587 | 1100 | 2.3723          |
| 2.4788        | 0.7186 | 1200 | 2.3302          |
| 2.6424        | 0.7784 | 1300 | 2.4048          |
| 2.6319        | 0.8383 | 1400 | 2.3805          |
| 2.5736        | 0.8982 | 1500 | 2.3620          |
| 2.5958        | 0.9581 | 1600 | 2.3191          |
| 2.2015        | 1.0180 | 1700 | 2.7917          |
| 1.8579        | 1.0778 | 1800 | 2.3981          |
| 1.7857        | 1.1377 | 1900 | 2.6140          |
| 1.7714        | 1.1976 | 2000 | 2.5079          |
| 1.7564        | 1.2575 | 2100 | 2.6297          |
| 1.8162        | 1.3174 | 2200 | 2.5810          |
| 1.7704        | 1.3772 | 2300 | 2.4192          |
| 1.8182        | 1.4371 | 2400 | 2.4714          |
| 1.8095        | 1.4970 | 2500 | 2.6516          |
| 1.9058        | 1.5569 | 2600 | 2.4050          |
| 1.8567        | 1.6168 | 2700 | 2.4032          |
| 1.7526        | 1.6766 | 2800 | 2.4313          |
| 1.6995        | 1.7365 | 2900 | 2.5849          |
| 1.7793        | 1.7964 | 3000 | 2.5520          |
| 1.7435        | 1.8563 | 3100 | 2.4718          |
| 1.8295        | 1.9162 | 3200 | 2.4131          |
| 1.8069        | 1.9760 | 3300 | 2.4696          |
| 1.4409        | 2.0359 | 3400 | 2.9758          |
| 1.2772        | 2.0958 | 3500 | 2.9478          |
| 1.1997        | 2.1557 | 3600 | 2.9533          |
| 1.19          | 2.2156 | 3700 | 2.8930          |
| 1.1831        | 2.2754 | 3800 | 3.0055          |
| 1.1424        | 2.3353 | 3900 | 3.0072          |
| 1.1612        | 2.3952 | 4000 | 2.9198          |
| 1.242         | 2.4551 | 4100 | 2.9932          |
| 1.1113        | 2.5150 | 4200 | 3.0195          |
| 1.2147        | 2.5749 | 4300 | 2.9441          |
| 1.1773        | 2.6347 | 4400 | 2.9556          |
| 1.2346        | 2.6946 | 4500 | 2.9006          |
| 1.1443        | 2.7545 | 4600 | 2.9683          |
| 1.2002        | 2.8144 | 4700 | 2.9800          |
| 1.1385        | 2.8743 | 4800 | 2.9573          |
| 1.2266        | 2.9341 | 4900 | 2.9599          |
| 1.2089        | 2.9940 | 5000 | 2.9656          |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1