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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: 0.8643440917174317
- name: F1
type: f1
value: 0.8368032657773605
---
<!-- 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: 1.0026
- Accuracy: 0.8643
- F1: 0.8368
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5131 | 0.27 | 5000 | 0.6674 | 0.8368 | 0.7780 |
| 0.3715 | 0.53 | 10000 | 0.6554 | 0.8527 | 0.8145 |
| 0.3066 | 0.8 | 15000 | 0.6924 | 0.8471 | 0.8103 |
| 0.2194 | 1.07 | 20000 | 0.7348 | 0.8548 | 0.8238 |
| 0.2112 | 1.34 | 25000 | 0.7297 | 0.8581 | 0.8288 |
| 0.1907 | 1.6 | 30000 | 0.7308 | 0.8558 | 0.8288 |
| 0.1816 | 1.87 | 35000 | 0.7785 | 0.8565 | 0.8281 |
| 0.1297 | 2.14 | 40000 | 0.8493 | 0.8567 | 0.8278 |
| 0.127 | 2.41 | 45000 | 0.8757 | 0.8576 | 0.8310 |
| 0.1148 | 2.67 | 50000 | 0.8581 | 0.8577 | 0.8300 |
| 0.1287 | 2.94 | 55000 | 0.8479 | 0.8597 | 0.8341 |
| 0.0875 | 3.21 | 60000 | 0.8763 | 0.8656 | 0.8392 |
| 0.0832 | 3.47 | 65000 | 0.9379 | 0.8620 | 0.8341 |
| 0.0837 | 3.74 | 70000 | 0.9044 | 0.8625 | 0.8339 |
| 0.0617 | 4.01 | 75000 | 0.9840 | 0.8618 | 0.8352 |
| 0.0524 | 4.28 | 80000 | 0.9955 | 0.8639 | 0.8385 |
| 0.0496 | 4.54 | 85000 | 1.0026 | 0.8643 | 0.8368 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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