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---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: all_1.1
split: validation
args: all_1.1
metrics:
- name: Accuracy
type: accuracy
value: 0.8577887926141738
- name: F1
type: f1
value: 0.8335554213502777
---
<!-- 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. -->
# scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9178
- Accuracy: 0.8578
- F1: 0.8336
## 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: 32
- eval_batch_size: 64
- seed: 66
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5269 | 0.27 | 5000 | 0.6875 | 0.8358 | 0.7817 |
| 0.3683 | 0.53 | 10000 | 0.6940 | 0.8489 | 0.8131 |
| 0.3073 | 0.8 | 15000 | 0.6710 | 0.8545 | 0.8198 |
| 0.2189 | 1.07 | 20000 | 0.7507 | 0.8539 | 0.8299 |
| 0.2276 | 1.34 | 25000 | 0.7456 | 0.8582 | 0.8347 |
| 0.1939 | 1.6 | 30000 | 0.8157 | 0.8562 | 0.8342 |
| 0.1852 | 1.87 | 35000 | 0.7920 | 0.8548 | 0.8269 |
| 0.1302 | 2.14 | 40000 | 0.8574 | 0.8559 | 0.8329 |
| 0.1273 | 2.41 | 45000 | 0.8945 | 0.8594 | 0.8330 |
| 0.1163 | 2.67 | 50000 | 0.9178 | 0.8578 | 0.8336 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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