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---
license: mit
language:
- ja
tags:
- PyTorch
- Transformers
---
## Japanese Stock Comment Sentiment Model
This model is a sentiment analysis tool specifically trained to analyze comments and discussions related to Japanese stocks. It is specialized in determining whether a comment has a bearish or bullish sentiment.
For its training, a large collection of individual stock-related comments was gathered, and these were categorized into two main categories: "bullish" and "bearish." This model can serve as a supportive tool for stock investors and market analysts in gathering information and making prompt decisions.
## How to use
### Part 1: Model Initialization
In this section, we'll be initializing the necessary components required for our prediction: the model and the tokenizer.
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the model and tokenizer
model_path = "c299m/japanese_stock_sentiment"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)
```
### Part 2: Text Prediction
Once our model and tokenizer are initialized, we can move on to predicting the sentiment of a given text. The sentiment is classified into two categories: "bullish" (positive sentiment) or "bearish" (negative sentiment).
```python
import numpy as np
import torch.nn.functional as F
# Text for inference
sample_text = "\
材料良すぎてストップ安、、助けてクレステック、、、\
"
# Tokenize the text
inputs = tokenizer(sample_text, return_tensors="pt")
# Set the model to evaluation mode
model.eval()
# Execute the inference
with torch.no_grad():
outputs = model(
inputs["input_ids"].to(device),
attention_mask=inputs["attention_mask"].to(device),
)
# Obtain logits and apply softmax function to convert to probabilities
probabilities = F.softmax(outputs.logits, dim=1).cpu().numpy()
# Get the index of the class with the highest probability
y_preds = np.argmax(probabilities, axis=1)
# Convert the index to a label
def id2label(x):
return model.config.id2label[x]
y_dash = [id2label(x) for x in y_preds]
# Get the probability of the most likely class
top_probs = probabilities[np.arange(len(y_preds)), y_preds]
print(y_dash, top_probs)
``` |