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--- |
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tags: |
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- generated_from_trainer |
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language: ja |
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widget: |
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- text: 🤗セグメント利益は、前期比8.3％増の24億28百万円となった |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: Japanese-sentiment-analysis |
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results: [] |
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datasets: |
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- jarvisx17/chABSA |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# japanese-sentiment-analysis |
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This model was trained from scratch on the chABSA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0001 |
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- Accuracy: 1.0 |
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- F1: 1.0 |
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## Model description |
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Model Train for Japanese sentence sentiments. |
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## Intended uses & limitations |
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The model was trained on chABSA Japanese dataset. |
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DATASET link : https://www.kaggle.com/datasets/takahirokubo0/chabsa |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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## Usage |
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You can use cURL to access this model: |
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Python API: |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("jarvisx17/japanese-sentiment-analysis") |
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model = AutoModelForSequenceClassification.from_pretrained("jarvisx17/japanese-sentiment-analysis") |
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inputs = tokenizer("I love AutoNLP", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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### Training results |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.2 |
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### Dependencies |
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- !pip install fugashi |
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- !pip install unidic_lite |
