|
--- |
|
license: mit |
|
pipeline_tag: text-classification |
|
--- |
|
# roberta-nei-fact-check |
|
|
|
This is a machine learning model trained for text classification using the Roberta architecture and a tokenizer. The purpose of this model is to identify whether a given claim with evidence contains enough information to make a fact-checking decision. |
|
|
|
## Model Details |
|
|
|
The model was trained using the Adam optimizer with a learning rate of 2-4e, an epsilon of 1-8, and a weight decay of 2-8e. The training data consisted mainly of the Fever and Hover datasets, with a small sample of created data. The model returns two labels: |
|
|
|
- 0: Enough information |
|
- 1: Not enough information |
|
|
|
The model uses a tokenizer for text classification and requires input in the form of a claim with evidence. This means that the input should be a text string containing both the claim and the evidence to provide best result. |
|
|
|
## Usage |
|
|
|
To use this model, you can load it into your Python code using a library such as PyTorch or TensorFlow. You can then pass in a claim with evidence string and the model will return a label indicating whether there is enough information in the claim with evidence for fact-checking. |
|
|
|
Here is an example of how to use the model in PyTorch: |
|
|
|
```python |
|
import torch |
|
from transformers import RobertaTokenizer, RobertaForSequenceClassification |
|
|
|
# Load the tokenizer and model |
|
tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/roberta-nei-fact-check') |
|
model = RobertaForSequenceClassification.from_pretrained('Dzeniks/roberta-nei-fact-check') |
|
|
|
# Define the claim with evidence to classify |
|
claim = "Albert Einstein work in the field of computer science" |
|
evidence = "Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time." |
|
|
|
# Tokenize the claim with evidence |
|
x = tokenizer.encode_plus(claim, evidence, return_tensors="pt") |
|
|
|
model.eval() |
|
with torch.no_grad(): |
|
prediction = model(**x) |
|
|
|
label = torch.argmax(outputs[0]).item() |
|
|
|
print(f"Label: {label}") |
|
``` |
|
In this example, the claim_with_evidence variable contains the claim with evidence to classify. The claim with evidence is tokenized using the tokenizer and converted to a tensor. The model is then used to classify the claim with evidence and the resulting label is printed to the console. |
