Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Raychanan/COVID_RandomOver")
model = AutoModelForSequenceClassification.from_pretrained("Raychanan/COVID_RandomOver")Quick Links
results
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4235
- F1: 0.9546
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|---|---|---|---|---|
| 1.1307 | 1.0 | 3268 | 0.9040 | 0.0 |
| 0.8795 | 2.0 | 6536 | 0.5532 | 0.9546 |
| 0.8183 | 3.0 | 9804 | 0.3641 | 0.9546 |
| 1.0074 | 4.0 | 13072 | 0.3998 | 0.9546 |
| 0.7947 | 5.0 | 16340 | 0.4235 | 0.9546 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Raychanan/COVID_RandomOver")