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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: base-vanilla-target-tweet
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: train
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.7780748663101604
- name: F1
type: f1
value: 0.7772664883136655
---
<!-- 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. -->
# base-vanilla-target-tweet
This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8380
- Accuracy: 0.7781
- F1: 0.7773
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3831 | 4.9 | 500 | 0.9800 | 0.7807 | 0.7785 |
| 0.0414 | 9.8 | 1000 | 1.4175 | 0.7754 | 0.7765 |
| 0.015 | 14.71 | 1500 | 1.6411 | 0.7754 | 0.7708 |
| 0.0166 | 19.61 | 2000 | 1.5930 | 0.7941 | 0.7938 |
| 0.0175 | 24.51 | 2500 | 1.3934 | 0.7888 | 0.7852 |
| 0.0191 | 29.41 | 3000 | 1.9407 | 0.7647 | 0.7658 |
| 0.0137 | 34.31 | 3500 | 1.8380 | 0.7781 | 0.7773 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
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