Instructions to use sakasa007/finetuning-sentiment-text-mining with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sakasa007/finetuning-sentiment-text-mining with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sakasa007/finetuning-sentiment-text-mining")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sakasa007/finetuning-sentiment-text-mining") model = AutoModelForSequenceClassification.from_pretrained("sakasa007/finetuning-sentiment-text-mining") - Notebooks
- Google Colab
- Kaggle
finetuning-sentiment-text-mining
- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840
Model description
This model was finetuned for course Text mining for Ai at Vrije university Amsterdam
Intended uses & limitations
Only use is for the final project in the course
Training and evaluation data
Data were reddit and twitter comments or tweets
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: 10
Training results
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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