File size: 2,215 Bytes
1dd9957 4bbdbbb 1dd9957 4bbdbbb 1dd9957 4bbdbbb 1dd9957 4bbdbbb 1dd9957 4bbdbbb 1dd9957 |
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 |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_qnli_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5881383855024712
---
<!-- 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. -->
# distilbert_sa_GLUE_Experiment_logit_kd_qnli_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3912
- Accuracy: 0.5881
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4059 | 1.0 | 410 | 0.3930 | 0.5733 |
| 0.3918 | 2.0 | 820 | 0.3919 | 0.5839 |
| 0.3807 | 3.0 | 1230 | 0.3912 | 0.5881 |
| 0.371 | 4.0 | 1640 | 0.3949 | 0.5843 |
| 0.3618 | 5.0 | 2050 | 0.3985 | 0.5815 |
| 0.352 | 6.0 | 2460 | 0.4136 | 0.5801 |
| 0.3416 | 7.0 | 2870 | 0.4222 | 0.5773 |
| 0.331 | 8.0 | 3280 | 0.4226 | 0.5742 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|