Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTPOS4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTPOS4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTPOS4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTPOS4") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTPOS4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/d5dda358196a587e0ab17a00a2da17f996c881a3/README.md
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4@d5dda358196a587e0ab17a00a2da17f996c881a3/README.md
-
curl -L -o README.md https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/d5dda358196a587e0ab17a00a2da17f996c881a3/README.md
1.71 kB
metadata
license: apache-2.0
base_model: Tanor/BERTicSENTPOS4
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: BERTicSENTPOS4
results: []
BERTicSENTPOS4
This model is a fine-tuned version of Tanor/BERTicSENTPOS4 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0806
- F1: 0.2581
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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|---|---|---|---|---|
| No log | 0.9843 | 47 | 0.0434 | 0.3333 |
| No log | 1.9895 | 95 | 0.0433 | 0.5238 |
| No log | 2.9948 | 143 | 0.0540 | 0.5490 |
| No log | 4.0 | 191 | 0.0566 | 0.5 |
| No log | 4.9843 | 238 | 0.0806 | 0.2581 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1