Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use emfa/danish-bert-botxo-danish-finetuned-hatespeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emfa/danish-bert-botxo-danish-finetuned-hatespeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emfa/danish-bert-botxo-danish-finetuned-hatespeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emfa/danish-bert-botxo-danish-finetuned-hatespeech") model = AutoModelForSequenceClassification.from_pretrained("emfa/danish-bert-botxo-danish-finetuned-hatespeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
danish-bert-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of Maltehb/danish-bert-botxo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3584
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 315 | 0.3285 |
| 0.2879 | 2.0 | 630 | 0.3288 |
| 0.2879 | 3.0 | 945 | 0.3178 |
| 0.1371 | 4.0 | 1260 | 0.3584 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
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