---
license: mit
base_model: csarron/bert-base-uncased-squad-v1
tags:
- generated_from_trainer
model-index:
- name: bert-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. -->

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

This model is a fine-tuned version of [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7077

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 4.3874        | 0.0599 | 100  | 3.3698          |
| 3.4284        | 0.1198 | 200  | 3.0414          |
| 3.1997        | 0.1796 | 300  | 2.9118          |
| 3.137         | 0.2395 | 400  | 2.8702          |
| 3.0928        | 0.2994 | 500  | 2.8431          |
| 3.1799        | 0.3593 | 600  | 2.8197          |
| 3.0335        | 0.4192 | 700  | 2.8069          |
| 3.0366        | 0.4790 | 800  | 2.7901          |
| 3.0403        | 0.5389 | 900  | 2.7791          |
| 3.0957        | 0.5988 | 1000 | 2.7762          |
| 3.1361        | 0.6587 | 1100 | 2.7784          |
| 2.9658        | 0.7186 | 1200 | 2.7671          |
| 3.0905        | 0.7784 | 1300 | 2.7583          |
| 3.0258        | 0.8383 | 1400 | 2.7524          |
| 3.0427        | 0.8982 | 1500 | 2.7471          |
| 2.9677        | 0.9581 | 1600 | 2.7434          |
| 2.9417        | 1.0180 | 1700 | 2.7501          |
| 3.011         | 1.0778 | 1800 | 2.7379          |
| 2.8598        | 1.1377 | 1900 | 2.7423          |
| 3.0521        | 1.1976 | 2000 | 2.7356          |
| 2.9869        | 1.2575 | 2100 | 2.7317          |
| 3.0301        | 1.3174 | 2200 | 2.7308          |
| 3.0015        | 1.3772 | 2300 | 2.7305          |
| 2.9257        | 1.4371 | 2400 | 2.7284          |
| 3.0083        | 1.4970 | 2500 | 2.7268          |
| 3.0781        | 1.5569 | 2600 | 2.7240          |
| 3.008         | 1.6168 | 2700 | 2.7262          |
| 3.0217        | 1.6766 | 2800 | 2.7192          |
| 2.9717        | 1.7365 | 2900 | 2.7154          |
| 2.964         | 1.7964 | 3000 | 2.7206          |
| 3.0208        | 1.8563 | 3100 | 2.7211          |
| 3.0612        | 1.9162 | 3200 | 2.7152          |
| 2.9425        | 1.9760 | 3300 | 2.7198          |
| 2.9976        | 2.0359 | 3400 | 2.7145          |
| 3.0736        | 2.0958 | 3500 | 2.7140          |
| 3.0291        | 2.1557 | 3600 | 2.7119          |
| 2.939         | 2.2156 | 3700 | 2.7098          |
| 2.9418        | 2.2754 | 3800 | 2.7119          |
| 2.9639        | 2.3353 | 3900 | 2.7139          |
| 3.0           | 2.3952 | 4000 | 2.7113          |
| 3.0245        | 2.4551 | 4100 | 2.7111          |
| 2.9465        | 2.5150 | 4200 | 2.7092          |
| 2.9164        | 2.5749 | 4300 | 2.7114          |
| 2.9692        | 2.6347 | 4400 | 2.7108          |
| 2.976         | 2.6946 | 4500 | 2.7091          |
| 2.9894        | 2.7545 | 4600 | 2.7076          |
| 2.9112        | 2.8144 | 4700 | 2.7074          |
| 2.9447        | 2.8743 | 4800 | 2.7077          |
| 2.9744        | 2.9341 | 4900 | 2.7073          |
| 2.9255        | 2.9940 | 5000 | 2.7077          |


### Framework versions

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