Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use papsebestyen/hubert-base-cc-finance-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papsebestyen/hubert-base-cc-finance-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papsebestyen/hubert-base-cc-finance-filter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papsebestyen/hubert-base-cc-finance-filter") model = AutoModelForSequenceClassification.from_pretrained("papsebestyen/hubert-base-cc-finance-filter") - Notebooks
- Google Colab
- Kaggle
hubert-base-cc-finance-filter
This model is a fine-tuned version of papsebestyen/hubert-base-cc-finetuned-forum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5388
- F1: 0.7671
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: 3.887995089067299e-05
- train_batch_size: 60
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 160.18013334673049
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5717 | 1.0 | 54 | 0.6918 | 0.624 |
| 0.4104 | 2.0 | 108 | 0.4236 | 0.7119 |
| 0.3124 | 3.0 | 162 | 0.6001 | 0.7451 |
| 0.1404 | 4.0 | 216 | 0.5388 | 0.7671 |
| 0.1305 | 5.0 | 270 | 0.5388 | 0.7671 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0a0+17540c5
- Datasets 2.2.1
- Tokenizers 0.12.1
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