transformers safetensors bert text-classification generated_from_trainer dataset:massive base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert/distilbert-base-uncased
datasets:
- massive
license: apache-2.0
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: results
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: test
      args: en-US
    metrics:
    - type: f1
      value: 0.9734295558770142
      name: F1
---

<!-- 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. -->

# results

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0231
- F1: 0.9734

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8235        | 0.5   | 185  | 3.7551          | 0.0022 |
| 3.5949        | 0.99  | 370  | 3.1246          | 0.0454 |
| 2.8705        | 1.49  | 555  | 2.4379          | 0.1543 |
| 2.3444        | 1.99  | 740  | 1.7732          | 0.2967 |
| 1.7151        | 2.49  | 925  | 1.2983          | 0.4403 |
| 1.3959        | 2.98  | 1110 | 0.9965          | 0.5490 |
| 0.9919        | 3.48  | 1295 | 0.7098          | 0.6880 |
| 0.9495        | 3.98  | 1480 | 0.5798          | 0.7014 |
| 0.6           | 4.48  | 1665 | 0.4419          | 0.7408 |
| 0.5952        | 4.97  | 1850 | 0.3653          | 0.7522 |
| 0.3715        | 5.47  | 2035 | 0.3077          | 0.7957 |
| 0.3783        | 5.97  | 2220 | 0.2050          | 0.8453 |
| 0.196         | 6.47  | 2405 | 0.1532          | 0.8386 |
| 0.22          | 6.96  | 2590 | 0.0968          | 0.8871 |
| 0.1117        | 7.46  | 2775 | 0.0725          | 0.9057 |
| 0.1065        | 7.96  | 2960 | 0.0458          | 0.9265 |
| 0.0644        | 8.45  | 3145 | 0.0378          | 0.9336 |
| 0.0526        | 8.95  | 3330 | 0.0324          | 0.9616 |
| 0.0521        | 9.45  | 3515 | 0.0251          | 0.9708 |
| 0.0302        | 9.95  | 3700 | 0.0231          | 0.9734 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
