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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_mrpc_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.32598039215686275
- name: F1
type: f1
value: 0.03508771929824561
---
<!-- 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_mrpc_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5217
- Accuracy: 0.3260
- F1: 0.0351
- Combined Score: 0.1805
## 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 | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5343 | 1.0 | 15 | 0.5288 | 0.3162 | 0.0 | 0.1581 |
| 0.5306 | 2.0 | 30 | 0.5289 | 0.3162 | 0.0 | 0.1581 |
| 0.5294 | 3.0 | 45 | 0.5281 | 0.3162 | 0.0 | 0.1581 |
| 0.5277 | 4.0 | 60 | 0.5269 | 0.3162 | 0.0 | 0.1581 |
| 0.518 | 5.0 | 75 | 0.5217 | 0.3260 | 0.0351 | 0.1805 |
| 0.5035 | 6.0 | 90 | 0.5230 | 0.3971 | 0.2635 | 0.3303 |
| 0.4866 | 7.0 | 105 | 0.5301 | 0.3652 | 0.1618 | 0.2635 |
| 0.4624 | 8.0 | 120 | 0.5491 | 0.5147 | 0.5123 | 0.5135 |
| 0.4424 | 9.0 | 135 | 0.5479 | 0.5245 | 0.5530 | 0.5388 |
| 0.4295 | 10.0 | 150 | 0.5660 | 0.5392 | 0.5766 | 0.5579 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
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