transformers pytorch deberta-v2 text-classification generated_from_trainer dataset:massive base_model:microsoft/mdeberta-v3-base base_model:finetune:microsoft/mdeberta-v3-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mdeberta-v3-base
datasets:
- massive
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-TCR_data-cl-massive_all_1_1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: all_1.1
      split: validation
      args: all_1.1
    metrics:
    - type: accuracy
      value: 0.7990745295221439
      name: Accuracy
    - type: f1
      value: 0.7564886611883039
      name: F1
---

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# scenario-TCR_data-cl-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: 1.3782
- Accuracy: 0.7991
- F1: 0.7565

## 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: 32
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4764        | 0.56  | 5000  | 0.9292          | 0.7898   | 0.7395 |
| 0.2624        | 1.11  | 10000 | 0.9850          | 0.7935   | 0.7392 |
| 0.2344        | 1.67  | 15000 | 1.0603          | 0.7976   | 0.7472 |
| 0.1556        | 2.22  | 20000 | 1.1387          | 0.7925   | 0.7431 |
| 0.1488        | 2.78  | 25000 | 1.1552          | 0.7960   | 0.7552 |
| 0.1048        | 3.33  | 30000 | 1.3310          | 0.7943   | 0.7452 |
| 0.101         | 3.89  | 35000 | 1.2902          | 0.7938   | 0.7516 |
| 0.0618        | 4.45  | 40000 | 1.3782          | 0.7991   | 0.7565 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
