Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bash1130/bert-base-finetuned-ynat")
model = AutoModelForSequenceClassification.from_pretrained("bash1130/bert-base-finetuned-ynat")Quick Links
bert-base-finetuned-ynat
This model is a fine-tuned version of klue/bert-base on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3609
- F1: 0.8712
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: 256
- eval_batch_size: 256
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 179 | 0.3979 | 0.8611 |
| No log | 2.0 | 358 | 0.3773 | 0.8669 |
| 0.3007 | 3.0 | 537 | 0.3609 | 0.8712 |
| 0.3007 | 4.0 | 716 | 0.3708 | 0.8708 |
| 0.3007 | 5.0 | 895 | 0.3720 | 0.8697 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Downloads last month
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Dataset used to train bash1130/bert-base-finetuned-ynat
Evaluation results
- F1 on klueself-reported0.871
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bash1130/bert-base-finetuned-ynat")