
# bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset.

## Pre-trained model

jarvisx17/japanese-sentiment-analysis<br/>
Link : https://huggingface.co/jarvisx17/japanese-sentiment-analysis

## Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.<br/>
link : https://www.cl.ecei.tohoku.ac.jp/Open_Resources-Japanese_Sentiment_Polarity_Dictionary.html

### 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: 10

## Usage

You can use cURL to access this model:

Python API:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("minutillamolinara/bert-japanese_finetuned-sentiment-analysis")

model = AutoModelForSequenceClassification.from_pretrained("minutillamolinara/bert-japanese_finetuned-sentiment-analysis")

inputs = tokenizer("自然言語処理が面白い", return_tensors="pt")

outputs = model(**inputs)
```

### Dependencies
- !pip install fugashi
- !pip install unidic_lite

## Licenses

MIT

{"language": "ja", "license": "mit", "metrics": ["accuracy", "f1"], "widget": [{"text": "\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u304c\u9762\u767d\u3044"}]}
transformers pytorch bert text-classification ja license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset.

## Pre-trained model

jarvisx17/japanese-sentiment-analysis<br/>
Link : URL

## Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.<br/>
link : URL

### 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: 10

## Usage

You can use cURL to access this model:

Python API:



### Dependencies
- !pip install fugashi
- !pip install unidic_lite

## Licenses

MIT

# bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset. ## Pre-trained model

jarvisx17/japanese-sentiment-analysis<br/>
Link : URL ## Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.<br/>
link : URL ### 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: 10 ## Usage

You can use cURL to access this model:

Python API: ### Dependencies
- !pip install fugashi
- !pip install unidic_lite ## Licenses

MIT
TAGS
#transformers #pytorch #bert #text-classification #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset. ## Pre-trained model

jarvisx17/japanese-sentiment-analysis<br/>
Link : URL ## Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.<br/>
link : URL ### 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: 10 ## Usage

You can use cURL to access this model:

Python API: ### Dependencies
- !pip install fugashi
- !pip install unidic_lite ## Licenses

MIT
TAGS
#transformers #pytorch #bert #text-classification #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset.## Pre-trained model

jarvisx17/japanese-sentiment-analysis<br/>
Link : URL## Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.<br/>
link : URL### 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: 10## Usage

You can use cURL to access this model:

Python API:### Dependencies
- !pip install fugashi
- !pip install unidic_lite## Licenses

MIT