File size: 1,985 Bytes
9c4d293 123e3dd 9c4d293 123e3dd 9c4d293 123e3dd 9c4d293 123e3dd 9c4d293 123e3dd 9c4d293 |
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 |
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_data_aug_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.49097472924187724
---
<!-- 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. -->
# hBERTv2_data_aug_rte
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3232
- Accuracy: 0.4910
## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6262 | 1.0 | 568 | 1.3232 | 0.4910 |
| 0.0855 | 2.0 | 1136 | 2.3457 | 0.4946 |
| 0.022 | 3.0 | 1704 | 2.9797 | 0.5018 |
| 0.0128 | 4.0 | 2272 | 2.6395 | 0.5271 |
| 0.0085 | 5.0 | 2840 | 3.1634 | 0.5379 |
| 0.0059 | 6.0 | 3408 | 3.5948 | 0.5199 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|