Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTPOS2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTPOS2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTPOS2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTPOS2") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTPOS2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Tanor/SRGPTSENTPOS2: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/0d00053f8556746e5989a0edec00d10eab63fd1c/README.md
- Command line
-
hf download hf://Tanor/SRGPTSENTPOS2@0d00053f8556746e5989a0edec00d10eab63fd1c/README.md
-
curl -L -o README.md https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/0d00053f8556746e5989a0edec00d10eab63fd1c/README.md
1.65 kB
metadata
license: apache-2.0
base_model: Tanor/SRGPTSENTPOS2
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: SRGPTSENTPOS2
results: []
SRGPTSENTPOS2
This model is a fine-tuned version of Tanor/SRGPTSENTPOS2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1934
- F1: 0.3437
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0896 | 1.0 | 2697 | 0.1886 | 0.3836 |
| 0.0767 | 2.0 | 5394 | 0.1254 | 0.3492 |
| 0.0598 | 3.0 | 8091 | 0.1355 | 0.3934 |
| 0.0155 | 4.0 | 10788 | 0.1635 | 0.3750 |
| 0.016 | 5.0 | 13485 | 0.1934 | 0.3437 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0.dev20230801
- Datasets 2.14.2
- Tokenizers 0.13.3