Annotate the dataset using current model
时间: 2024-01-18 21:02:57 浏览: 25
Sure, I can help you with that. To annotate a dataset using the current model, we can follow these steps:
1. Load the dataset and the current model.
2. Iterate through the dataset and apply the model to each data point.
3. Extract the output from the model and add it as a new column to the dataset.
Here's some sample Python code that demonstrates this process:
```
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the dataset and the model
df = pd.read_csv("dataset.csv")
tokenizer = AutoTokenizer.from_pretrained("model_name")
model = AutoModelForCausalLM.from_pretrained("model_name")
# Define a function to apply the model to a single data point
def annotate_data_point(text):
input_ids = tokenizer.encode(text, return_tensors="pt")
output = model.generate(input_ids)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
return output_text
# Apply the model to each data point in the dataset
df["model_output"] = df["text"].apply(annotate_data_point)
# Save the annotated dataset
df.to_csv("annotated_dataset.csv", index=False)
```
In this example, we're using the Transformers library to load the tokenizer and the model. We're also assuming that the dataset is stored in a CSV file with a column called "text". The output from the model is added as a new column called "model_output". Finally, we save the annotated dataset to a new CSV file.