Instructions to use dwmit/ja_classification_brl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/ja_classification_brl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dwmit/ja_classification_brl")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dwmit/ja_classification_brl") model = AutoModelForTokenClassification.from_pretrained("dwmit/ja_classification_brl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,344 Bytes
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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ja_classification_brl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ja_classification_brl
This model is a fine-tuned version of [dicta-il/BEREL_2.0](https://huggingface.co/dicta-il/BEREL_2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0712
- Precision: 0.9846
- Recall: 0.9846
- F1: 0.9846
- Accuracy: 0.9846
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 1125 | 0.0522 | 0.9819 | 0.9819 | 0.9819 | 0.9819 |
| No log | 2.0 | 2250 | 0.0490 | 0.9837 | 0.9837 | 0.9837 | 0.9837 |
| No log | 3.0 | 3375 | 0.0481 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
| No log | 4.0 | 4500 | 0.0514 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
| No log | 5.0 | 5625 | 0.0548 | 0.9848 | 0.9848 | 0.9848 | 0.9848 |
| No log | 6.0 | 6750 | 0.0587 | 0.9846 | 0.9846 | 0.9846 | 0.9846 |
| No log | 7.0 | 7875 | 0.0636 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
| No log | 8.0 | 9000 | 0.0669 | 0.9846 | 0.9846 | 0.9846 | 0.9846 |
| No log | 9.0 | 10125 | 0.0685 | 0.9844 | 0.9844 | 0.9844 | 0.9844 |
| No log | 10.0 | 11250 | 0.0712 | 0.9846 | 0.9846 | 0.9846 | 0.9846 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.0+cu117
- Datasets 2.11.0
- Tokenizers 0.11.6
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