Description:
Classify student assessment results to monitor their progress and identify areas that require improvement.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/student_progress_tracking_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
Runs of interneuronai student_progress_tracking_bart on huggingface.co
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student_progress_tracking_bart huggingface.co is an AI model on huggingface.co that provides student_progress_tracking_bart's model effect (), which can be used instantly with this interneuronai student_progress_tracking_bart model. huggingface.co supports a free trial of the student_progress_tracking_bart model, and also provides paid use of the student_progress_tracking_bart. Support call student_progress_tracking_bart model through api, including Node.js, Python, http.
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student_progress_tracking_bart is an open source model from GitHub that offers a free installation service, and any user can find student_progress_tracking_bart on GitHub to install. At the same time, huggingface.co provides the effect of student_progress_tracking_bart install, users can directly use student_progress_tracking_bart installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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