---
license: other
widget:
- text: I'm fine. Who is this?
- text: You can't take anything seriously.
- text: In the end he's going to croak, isn't he?
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: balanced-augmented-distilbert-gest-pred-seqeval-partialmatch
  results: []
pipeline_tag: token-classification
datasets:
- Jsevisal/balanced_augmented_dataset
---

<!-- 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. -->

# balanced-augmented-distilbert-gest-pred-seqeval-partialmatch

This model is a fine-tuned version of [elastic/distilbert-base-cased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8831
- Precision: 0.8268
- Recall: 0.8056
- F1: 0.8096
- Accuracy: 0.7890

## 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: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 3.3759        | 1.0   | 32   | 2.8926          | 0.0638    | 0.0673 | 0.0464 | 0.2120   |
| 2.771         | 2.0   | 64   | 2.2857          | 0.4028    | 0.2440 | 0.2402 | 0.3981   |
| 2.1033        | 3.0   | 96   | 1.8025          | 0.5923    | 0.4516 | 0.4646 | 0.5434   |
| 1.5324        | 4.0   | 128  | 1.4866          | 0.6961    | 0.5570 | 0.5709 | 0.6293   |
| 1.1576        | 5.0   | 160  | 1.2689          | 0.7556    | 0.6457 | 0.6624 | 0.6779   |
| 0.8788        | 6.0   | 192  | 1.1505          | 0.7568    | 0.6992 | 0.7069 | 0.7011   |
| 0.6714        | 7.0   | 224  | 1.0633          | 0.7887    | 0.7405 | 0.7470 | 0.7260   |
| 0.5171        | 8.0   | 256  | 1.0177          | 0.8137    | 0.7512 | 0.7666 | 0.7528   |
| 0.4106        | 9.0   | 288  | 0.9926          | 0.8054    | 0.7660 | 0.7724 | 0.7410   |
| 0.3163        | 10.0  | 320  | 0.9222          | 0.8063    | 0.7813 | 0.7851 | 0.7653   |
| 0.2589        | 11.0  | 352  | 0.8984          | 0.8179    | 0.8018 | 0.8027 | 0.7834   |
| 0.2133        | 12.0  | 384  | 0.8848          | 0.8217    | 0.8020 | 0.8053 | 0.7844   |
| 0.1736        | 13.0  | 416  | 0.8831          | 0.8268    | 0.8056 | 0.8096 | 0.7890   |
| 0.148         | 14.0  | 448  | 0.9212          | 0.8386    | 0.8152 | 0.8173 | 0.7932   |
| 0.1301        | 15.0  | 480  | 0.9115          | 0.8242    | 0.8098 | 0.8092 | 0.7896   |
| 0.1104        | 16.0  | 512  | 0.9095          | 0.8230    | 0.8123 | 0.8104 | 0.7844   |
| 0.1016        | 17.0  | 544  | 0.9210          | 0.8334    | 0.8089 | 0.8124 | 0.7839   |
| 0.0911        | 18.0  | 576  | 0.9234          | 0.8408    | 0.8126 | 0.8170 | 0.7890   |
| 0.0883        | 19.0  | 608  | 0.9177          | 0.8349    | 0.8106 | 0.8135 | 0.7885   |
| 0.0848        | 20.0  | 640  | 0.9256          | 0.8357    | 0.8106 | 0.8136 | 0.7890   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

### LICENSE
Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados.
Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos.