YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Arcane-BART

Arcane-BART is a lightweight, efficient sequence-to-sequence model designed for fast mental health screening and real-time distress analysis.

Overview

Type: Seq2Seq Transformer (~1.6 GB)
Deployment: Suitable for local CPU inference, containers, and edge backends.

Inference Example

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "ar3xop/arcane-bart"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

prompt = 'Consider this post: "The stress from examinations is unbearable." Question: What is the cause of stress?'
inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)

output_ids = model.generate(**inputs, max_length=256)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

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