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csocsci/
xlm-roberta-large-binary-en-iib

Name: xlm-roberta-large-binary-en-iib
Availability: InStock

text-embeddings-inference

endpoints_compatible

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Model Card for xlm-roberta-large-binary-en-iib

This model is fine-tuned for binary text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents.

Model Description

The model was fine-tuned on a dataset of Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs probablities for labels {0,1}: Supportive Interactions present or not.

Developed by: Anonymous
Language(s): multilingual
Finetuned from: xlm-roberta-large

Model Sources

Repository: https://github.com/csocsci/supportive-interactions
Paper: Stay tuned!

Usage

Here is how to use this model to classify a context-window of a dialogue:

python

import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Prepare input texts. This model is pretrained and fine-tuned for Czech
test_texts = ['Utterance1;Utterance2;Utterance3']

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
    'csocsci/xlm-roberta-large-binary-en-iib', num_labels=2).to("cuda")

tokenizer = AutoTokenizer.from_pretrained(
    'csocsci/xlm-roberta-large-binary-en-iib',
    use_fast=False, truncation_side='left')
assert tokenizer.truncation_side == 'left'

# Define helper functions
def get_probs(text, tokenizer, model):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=256,
                       return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    return outputs[0].softmax(1)

def preds2class(probs, threshold=0.5):
    pclasses = np.zeros(probs.shape)
    pclasses[np.where(probs >= threshold)] = 1
    return pclasses.argmax(-1)

def print_predictions(texts):
    probabilities = [get_probs(
        texts[i], tokenizer, model).cpu().detach().numpy()[0]
                     for i in range(len(texts))]
    predicted_classes = preds2class(np.array(probabilities))
    for c, p in zip(predicted_classes, probabilities):
        print(f'{c}: {p}')

# Run the prediction
print_predictions(test_texts)