Instructions to use simjo/model1_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simjo/model1_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simjo/model1_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simjo/model1_test") model = AutoModelForSequenceClassification.from_pretrained("simjo/model1_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from simjo/model1_test: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/simjo/model1_test/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://simjo/model1_test@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/simjo/model1_test/resolve/refs%2Fpr%2F1/README.md
1.59 kB
metadata
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: DaNLP/da-bert-hatespeech-detection
model-index:
- name: model1_test
results: []
model1_test
This model is a fine-tuned version of DaNLP/da-bert-hatespeech-detection on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1816
- Accuracy: 0.9667
- F1: 0.3548
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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 150 | 0.1128 | 0.9667 | 0.2 |
| No log | 2.0 | 300 | 0.1666 | 0.9684 | 0.2963 |
| No log | 3.0 | 450 | 0.1816 | 0.9667 | 0.3548 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3