tensorboard safetensors generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: QA_using_DistilBERT_LORA_qv
  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. -->

# QA_using_DistilBERT_LORA_qv

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7782

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.7211        | 0.01  | 500   | 4.4152          |
| 4.3014        | 0.03  | 1000  | 4.3057          |
| 4.2235        | 0.04  | 1500  | 4.2254          |
| 4.1424        | 0.06  | 2000  | 4.1592          |
| 4.1312        | 0.07  | 2500  | 4.1091          |
| 4.033         | 0.09  | 3000  | 3.8720          |
| 3.739         | 0.1   | 3500  | 3.7028          |
| 3.6547        | 0.12  | 4000  | 3.5784          |
| 3.4915        | 0.13  | 4500  | 3.4967          |
| 3.5266        | 0.15  | 5000  | 3.4501          |
| 3.4602        | 0.16  | 5500  | 3.5048          |
| 3.4749        | 0.18  | 6000  | 3.3635          |
| 3.4088        | 0.19  | 6500  | 3.3465          |
| 3.3869        | 0.21  | 7000  | 3.3438          |
| 3.3835        | 0.22  | 7500  | 3.2838          |
| 3.2902        | 0.23  | 8000  | 3.3156          |
| 3.2747        | 0.25  | 8500  | 3.2770          |
| 3.2968        | 0.26  | 9000  | 3.2578          |
| 3.2305        | 0.28  | 9500  | 3.2645          |
| 3.2288        | 0.29  | 10000 | 3.1857          |
| 3.2717        | 0.31  | 10500 | 3.2326          |
| 3.1697        | 0.32  | 11000 | 3.2098          |
| 3.1786        | 0.34  | 11500 | 3.2656          |
| 3.2063        | 0.35  | 12000 | 3.1725          |
| 3.186         | 0.37  | 12500 | 3.1901          |
| 3.1389        | 0.38  | 13000 | 3.1706          |
| 3.234         | 0.4   | 13500 | 3.1553          |
| 3.1207        | 0.41  | 14000 | 3.1764          |
| 3.1764        | 0.42  | 14500 | 3.1441          |
| 3.1458        | 0.44  | 15000 | 3.1459          |
| 3.0631        | 0.45  | 15500 | 3.1461          |
| 3.1193        | 0.47  | 16000 | 3.1306          |
| 3.0437        | 0.48  | 16500 | 3.1775          |
| 3.1309        | 0.5   | 17000 | 3.0853          |
| 3.0448        | 0.51  | 17500 | 3.1136          |
| 3.0273        | 0.53  | 18000 | 3.0640          |
| 3.0826        | 0.54  | 18500 | 3.0786          |
| 3.0044        | 0.56  | 19000 | 3.0843          |
| 3.0672        | 0.57  | 19500 | 3.0516          |
| 3.0447        | 0.59  | 20000 | 3.0581          |
| 3.0168        | 0.6   | 20500 | 3.0369          |
| 2.9619        | 0.62  | 21000 | 3.0725          |
| 3.0981        | 0.63  | 21500 | 3.0389          |
| 3.0247        | 0.64  | 22000 | 3.0339          |
| 3.041         | 0.66  | 22500 | 3.0465          |
| 3.0286        | 0.67  | 23000 | 3.0806          |
| 3.0136        | 0.69  | 23500 | 3.0149          |
| 2.9814        | 0.7   | 24000 | 3.0128          |
| 3.0359        | 0.72  | 24500 | 3.0086          |
| 2.9939        | 0.73  | 25000 | 3.0216          |
| 2.996         | 0.75  | 25500 | 3.1415          |
| 2.9554        | 0.76  | 26000 | 3.0490          |
| 2.9773        | 0.78  | 26500 | 3.0457          |
| 2.9625        | 0.79  | 27000 | 2.9663          |
| 2.9184        | 0.81  | 27500 | 2.9981          |
| 2.9735        | 0.82  | 28000 | 3.0404          |
| 2.9567        | 0.84  | 28500 | 2.9621          |
| 2.9706        | 0.85  | 29000 | 3.0024          |
| 2.9436        | 0.86  | 29500 | 2.9535          |
| 2.9069        | 0.88  | 30000 | 2.9993          |
| 2.9652        | 0.89  | 30500 | 2.9393          |
| 2.9426        | 0.91  | 31000 | 2.9693          |
| 2.8936        | 0.92  | 31500 | 2.9111          |
| 2.9245        | 0.94  | 32000 | 2.9678          |
| 2.9054        | 0.95  | 32500 | 2.9263          |
| 2.8426        | 0.97  | 33000 | 2.9429          |
| 2.8782        | 0.98  | 33500 | 2.9232          |
| 2.8963        | 1.0   | 34000 | 2.9545          |
| 2.8757        | 1.01  | 34500 | 2.9181          |
| 2.853         | 1.03  | 35000 | 2.8925          |
| 2.8758        | 1.04  | 35500 | 2.9464          |
| 2.9179        | 1.06  | 36000 | 2.9076          |
| 2.8924        | 1.07  | 36500 | 2.8874          |
| 2.9488        | 1.08  | 37000 | 2.9284          |
| 2.8746        | 1.1   | 37500 | 2.9012          |
| 2.8026        | 1.11  | 38000 | 2.8679          |
| 2.8177        | 1.13  | 38500 | 2.9000          |
| 2.8113        | 1.14  | 39000 | 2.9069          |
| 2.8047        | 1.16  | 39500 | 2.8755          |
| 2.8437        | 1.17  | 40000 | 2.9043          |
| 2.8093        | 1.19  | 40500 | 2.8915          |
| 2.7881        | 1.2   | 41000 | 2.8665          |
| 2.8251        | 1.22  | 41500 | 2.8516          |
| 2.8356        | 1.23  | 42000 | 2.8927          |
| 2.7805        | 1.25  | 42500 | 2.8759          |
| 2.8944        | 1.26  | 43000 | 2.8491          |
| 2.88          | 1.27  | 43500 | 2.8458          |
| 2.8109        | 1.29  | 44000 | 2.8613          |
| 2.7595        | 1.3   | 44500 | 2.8734          |
| 2.8038        | 1.32  | 45000 | 2.8344          |
| 2.8113        | 1.33  | 45500 | 2.8448          |
| 2.8396        | 1.35  | 46000 | 2.8216          |
| 2.833         | 1.36  | 46500 | 2.8445          |
| 2.7711        | 1.38  | 47000 | 2.8499          |
| 2.7933        | 1.39  | 47500 | 2.8649          |
| 2.8079        | 1.41  | 48000 | 2.8390          |
| 2.781         | 1.42  | 48500 | 2.7999          |
| 2.8195        | 1.44  | 49000 | 2.8320          |
| 2.7553        | 1.45  | 49500 | 2.8500          |
| 2.7769        | 1.47  | 50000 | 2.8364          |
| 2.6745        | 1.48  | 50500 | 2.8392          |
| 2.7891        | 1.49  | 51000 | 2.8166          |
| 2.7691        | 1.51  | 51500 | 2.8195          |
| 2.7744        | 1.52  | 52000 | 2.8505          |
| 2.739         | 1.54  | 52500 | 2.8055          |
| 2.7843        | 1.55  | 53000 | 2.8633          |
| 2.7072        | 1.57  | 53500 | 2.8214          |
| 2.7658        | 1.58  | 54000 | 2.8178          |
| 2.7271        | 1.6   | 54500 | 2.8075          |
| 2.8387        | 1.61  | 55000 | 2.8025          |
| 2.7425        | 1.63  | 55500 | 2.8061          |
| 2.7464        | 1.64  | 56000 | 2.7882          |
| 2.7442        | 1.66  | 56500 | 2.8161          |
| 2.7398        | 1.67  | 57000 | 2.8091          |
| 2.7081        | 1.69  | 57500 | 2.8166          |
| 2.759         | 1.7   | 58000 | 2.8014          |
| 2.6873        | 1.71  | 58500 | 2.7949          |
| 2.8057        | 1.73  | 59000 | 2.8044          |
| 2.8156        | 1.74  | 59500 | 2.7860          |
| 2.6884        | 1.76  | 60000 | 2.7931          |
| 2.7627        | 1.77  | 60500 | 2.7931          |
| 2.6991        | 1.79  | 61000 | 2.7895          |
| 2.8059        | 1.8   | 61500 | 2.7981          |
| 2.7018        | 1.82  | 62000 | 2.7972          |
| 2.7027        | 1.83  | 62500 | 2.7956          |
| 2.7658        | 1.85  | 63000 | 2.7949          |
| 2.7735        | 1.86  | 63500 | 2.7803          |
| 2.6972        | 1.88  | 64000 | 2.7894          |
| 2.6512        | 1.89  | 64500 | 2.8087          |
| 2.6856        | 1.9   | 65000 | 2.7795          |
| 2.7292        | 1.92  | 65500 | 2.7772          |
| 2.7744        | 1.93  | 66000 | 2.7821          |
| 2.8022        | 1.95  | 66500 | 2.7858          |
| 2.7054        | 1.96  | 67000 | 2.7816          |
| 2.7255        | 1.98  | 67500 | 2.7740          |
| 2.6243        | 1.99  | 68000 | 2.7782          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
