|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- dutch_social |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: robbert-twitter-sentiment-tokenized |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: dutch_social |
|
type: dutch_social |
|
args: dutch_social |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.814 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8132800039281481 |
|
- name: Precision |
|
type: precision |
|
value: 0.8131073640029836 |
|
- name: Recall |
|
type: recall |
|
value: 0.814 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# robbert-twitter-sentiment-tokenized |
|
|
|
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5473 |
|
- Accuracy: 0.814 |
|
- F1: 0.8133 |
|
- Precision: 0.8131 |
|
- Recall: 0.814 |
|
|
|
## 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: 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: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.6895 | 1.0 | 282 | 0.6307 | 0.7433 | 0.7442 | 0.7500 | 0.7433 | |
|
| 0.4948 | 2.0 | 564 | 0.5189 | 0.8053 | 0.8062 | 0.8081 | 0.8053 | |
|
| 0.2642 | 3.0 | 846 | 0.5473 | 0.814 | 0.8133 | 0.8131 | 0.814 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0 |
|
- Pytorch 1.11.0+cpu |
|
- Datasets 2.0.0 |
|
- Tokenizers 0.11.6 |
|
|
