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---
license: mit
tags:
- generated_from_trainer
datasets:
- tweets_hate_speech_detection
metrics:
- accuracy
- f1
model-index:
- name: FirstTry
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweets_hate_speech_detection
type: tweets_hate_speech_detection
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9759098967567004
- name: F1
type: f1
value: 0.8034042553191489
---
<!-- 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. -->
# FirstTry
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweets_hate_speech_detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0977
- Accuracy: 0.9759
- F1: 0.8034
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.04 | 50 | 0.2125 | 0.9337 | 0.0 |
| No log | 0.07 | 100 | 0.2210 | 0.9341 | 0.0125 |
| No log | 0.11 | 150 | 0.1832 | 0.9554 | 0.5103 |
| No log | 0.14 | 200 | 0.1539 | 0.9583 | 0.6377 |
| No log | 0.18 | 250 | 0.2435 | 0.9523 | 0.4434 |
| No log | 0.21 | 300 | 0.1818 | 0.9589 | 0.5736 |
| No log | 0.25 | 350 | 0.1138 | 0.9618 | 0.7136 |
| No log | 0.29 | 400 | 0.1045 | 0.9667 | 0.7243 |
| No log | 0.32 | 450 | 0.0958 | 0.9676 | 0.7330 |
| 0.1788 | 0.36 | 500 | 0.0935 | 0.9695 | 0.7306 |
| 0.1788 | 0.39 | 550 | 0.1289 | 0.9666 | 0.7178 |
| 0.1788 | 0.43 | 600 | 0.1039 | 0.9648 | 0.7507 |
| 0.1788 | 0.46 | 650 | 0.1234 | 0.9646 | 0.6435 |
| 0.1788 | 0.5 | 700 | 0.0984 | 0.9703 | 0.7725 |
| 0.1788 | 0.54 | 750 | 0.1364 | 0.9702 | 0.7185 |
| 0.1788 | 0.57 | 800 | 0.1004 | 0.9739 | 0.7792 |
| 0.1788 | 0.61 | 850 | 0.0998 | 0.9684 | 0.7616 |
| 0.1788 | 0.64 | 900 | 0.1068 | 0.9738 | 0.7857 |
| 0.1788 | 0.68 | 950 | 0.1206 | 0.9732 | 0.7644 |
| 0.1198 | 0.71 | 1000 | 0.0977 | 0.9759 | 0.8034 |
| 0.1198 | 0.75 | 1050 | 0.0864 | 0.9742 | 0.7916 |
| 0.1198 | 0.79 | 1100 | 0.1297 | 0.9727 | 0.7849 |
| 0.1198 | 0.82 | 1150 | 0.0969 | 0.9751 | 0.8026 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.10.1
- Tokenizers 0.13.3
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