File size: 3,027 Bytes
7197215 dee7adb 7197215 dee7adb 7197215 dee7adb 7197215 dee7adb 7197215 dee7adb 7197215 |
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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-mnli_IndE
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. -->
# roberta-base-mnli_IndE
This model is a fine-tuned version of [WillHeld/roberta-base-mnli](https://huggingface.co/WillHeld/roberta-base-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7633
- Acc: 0.8517
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3903 | 0.17 | 2000 | 0.4502 | 0.8359 |
| 0.3776 | 0.33 | 4000 | 0.4488 | 0.8378 |
| 0.3694 | 0.5 | 6000 | 0.4400 | 0.8408 |
| 0.3679 | 0.67 | 8000 | 0.4412 | 0.8395 |
| 0.3584 | 0.83 | 10000 | 0.4079 | 0.8514 |
| 0.3618 | 1.0 | 12000 | 0.4326 | 0.8433 |
| 0.2582 | 1.17 | 14000 | 0.4738 | 0.8459 |
| 0.2603 | 1.33 | 16000 | 0.4921 | 0.8468 |
| 0.2608 | 1.5 | 18000 | 0.4542 | 0.8498 |
| 0.2591 | 1.67 | 20000 | 0.4709 | 0.8483 |
| 0.263 | 1.83 | 22000 | 0.4955 | 0.8466 |
| 0.2611 | 2.0 | 24000 | 0.4829 | 0.8513 |
| 0.1802 | 2.17 | 26000 | 0.5470 | 0.8493 |
| 0.1819 | 2.33 | 28000 | 0.5523 | 0.8503 |
| 0.1847 | 2.5 | 30000 | 0.5160 | 0.8519 |
| 0.1886 | 2.67 | 32000 | 0.5229 | 0.8521 |
| 0.1877 | 2.83 | 34000 | 0.5024 | 0.8528 |
| 0.1839 | 3.0 | 36000 | 0.5456 | 0.8536 |
| 0.1322 | 3.17 | 38000 | 0.6997 | 0.8492 |
| 0.1385 | 3.33 | 40000 | 0.6212 | 0.8534 |
| 0.1326 | 3.5 | 42000 | 0.6629 | 0.8529 |
| 0.1355 | 3.67 | 44000 | 0.6448 | 0.8516 |
| 0.1332 | 3.83 | 46000 | 0.6411 | 0.8544 |
| 0.1372 | 4.0 | 48000 | 0.6574 | 0.8526 |
| 0.1056 | 4.17 | 50000 | 0.7427 | 0.8529 |
| 0.1053 | 4.33 | 52000 | 0.7466 | 0.8518 |
| 0.1062 | 4.5 | 54000 | 0.7734 | 0.8536 |
| 0.1056 | 4.67 | 56000 | 0.7623 | 0.8518 |
| 0.1072 | 4.83 | 58000 | 0.7633 | 0.8517 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|