File size: 2,060 Bytes
9a0d0d6 cf22b77 9a0d0d6 8d4ec80 9a0d0d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets: pietrolesci/nli_fever
metrics:
- f1
model-index:
- name: destilbert_uncased_fever_nli
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. -->
# destilbert_uncased_fever_nli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of [fever_nli](https://huggingface.co/datasets/pietrolesci/nli_fever) dataset by using the first 7.5k datapoints per each label from the training split.
It achieves the following results on the evaluation set:
- Loss: 2.1829
- F1: 0.7045
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 352 | 0.7894 | 0.7029 |
| 0.5462 | 2.0 | 704 | 0.9908 | 0.7097 |
| 0.2922 | 3.0 | 1056 | 1.0831 | 0.6924 |
| 0.2922 | 4.0 | 1408 | 1.2833 | 0.7044 |
| 0.142 | 5.0 | 1760 | 1.4096 | 0.7008 |
| 0.0695 | 6.0 | 2112 | 1.5585 | 0.7013 |
| 0.0695 | 7.0 | 2464 | 1.7262 | 0.7015 |
| 0.0434 | 8.0 | 2816 | 2.0138 | 0.7016 |
| 0.0204 | 9.0 | 3168 | 2.0912 | 0.7012 |
| 0.011 | 10.0 | 3520 | 2.1829 | 0.7045 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|