# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("kktoto/ty_punctuator")
model = AutoModelForTokenClassification.from_pretrained("kktoto/ty_punctuator")Quick Links
ty_punctuator
This model is a fine-tuned version of kktoto/kt_punc on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0937
- Precision: 0.7436
- Recall: 0.7694
- F1: 0.7563
- Accuracy: 0.9656
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0967 | 1.0 | 5561 | 0.0937 | 0.7436 | 0.7694 | 0.7563 | 0.9656 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kktoto/ty_punctuator")