---
base_model: SpanBERT/spanbert-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: aspect_complaint_spanbert
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# aspect_complaint_spanbert

This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2032
- F1: 0.8368
- Roc Auc: 0.8935
- Accuracy: 0.5292

## 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 97   | 0.2810          | 0.6988 | 0.7718  | 0.0      |
| No log        | 2.0   | 194  | 0.2209          | 0.7688 | 0.8260  | 0.2577   |
| No log        | 3.0   | 291  | 0.2017          | 0.7958 | 0.8477  | 0.3565   |
| No log        | 4.0   | 388  | 0.1882          | 0.8120 | 0.8678  | 0.4184   |
| No log        | 5.0   | 485  | 0.1773          | 0.8272 | 0.8693  | 0.4416   |
| 0.2417        | 6.0   | 582  | 0.1732          | 0.8262 | 0.8792  | 0.4811   |
| 0.2417        | 7.0   | 679  | 0.1719          | 0.8331 | 0.8795  | 0.4759   |
| 0.2417        | 8.0   | 776  | 0.1722          | 0.8341 | 0.8814  | 0.5017   |
| 0.2417        | 9.0   | 873  | 0.1797          | 0.8347 | 0.8818  | 0.4923   |
| 0.2417        | 10.0  | 970  | 0.1856          | 0.8328 | 0.8859  | 0.5112   |
| 0.1287        | 11.0  | 1067 | 0.1866          | 0.8343 | 0.8868  | 0.5275   |
| 0.1287        | 12.0  | 1164 | 0.1868          | 0.8378 | 0.8898  | 0.5258   |
| 0.1287        | 13.0  | 1261 | 0.1923          | 0.8322 | 0.8864  | 0.5120   |
| 0.1287        | 14.0  | 1358 | 0.1916          | 0.8400 | 0.8929  | 0.5387   |
| 0.1287        | 15.0  | 1455 | 0.1975          | 0.8368 | 0.8919  | 0.5421   |
| 0.0781        | 16.0  | 1552 | 0.1974          | 0.8403 | 0.8951  | 0.5369   |
| 0.0781        | 17.0  | 1649 | 0.2022          | 0.8329 | 0.8911  | 0.5241   |
| 0.0781        | 18.0  | 1746 | 0.2012          | 0.8360 | 0.8921  | 0.5284   |
| 0.0781        | 19.0  | 1843 | 0.2027          | 0.8367 | 0.8936  | 0.5335   |
| 0.0781        | 20.0  | 1940 | 0.2032          | 0.8368 | 0.8935  | 0.5292   |


### Framework versions

- Transformers 4.41.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1