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---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for small-e-czech-binary-online-risks-cs
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for binary text classification of Online Risks in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is binary and the model outputs probablities for labels {0,1}: Online Risks present or not.
- **Developed by:** Anonymous
- **Language(s):** cs
- **Finetuned from:** Seznam/small-e-czech
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/justtherightsize/supportive-interactions-and-risks
- **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 fine-tuned for Czech
test_texts = ['Utterance1;Utterance2;Utterance3']
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
'justtherightsize/small-e-czech-binary-online-risks-cs', num_labels=2).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
'justtherightsize/small-e-czech-binary-online-risks-cs',
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)
``` |