Update README

.ipynb_checkpoints/README-checkpoint.md

@@ -7,7 +7,69 @@ tags:
 - Transformers
 ---
 
-Japanese Stock Comment Sentiment Model
+## 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)
+```

README.md

@@ -7,7 +7,69 @@ tags:
 - Transformers
 ---
 
-Japanese Stock Comment Sentiment Model
+## 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)
+```