Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("honzapucalek/emotion-analysis-mBERT-czech")
model = AutoModelForSequenceClassification.from_pretrained("honzapucalek/emotion-analysis-mBERT-czech", device_map="auto")Quick Links
emotion-analysis-mBERT-czech
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1507
- F1: 0.8702
- Roc Auc: 0.9218
- Accuracy: 0.863
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1653 | 1.0 | 2000 | 0.1560 | 0.8306 | 0.8884 | 0.801 |
| 0.1305 | 2.0 | 4000 | 0.1334 | 0.8571 | 0.9073 | 0.8345 |
| 0.093 | 3.0 | 6000 | 0.1389 | 0.8540 | 0.9099 | 0.8395 |
| 0.0713 | 4.0 | 8000 | 0.1437 | 0.8694 | 0.9202 | 0.862 |
| 0.0605 | 5.0 | 10000 | 0.1507 | 0.8702 | 0.9218 | 0.863 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for honzapucalek/emotion-analysis-mBERT-czech
Base model
google-bert/bert-base-multilingual-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="honzapucalek/emotion-analysis-mBERT-czech")