# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("vatsal18/multi-lang_summay")
model = AutoModelForSeq2SeqLM.from_pretrained("vatsal18/multi-lang_summay")Quick Links
multi-lang_summay
Fine-tuned seq2seq model for multilingual abstractive summarization.
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
repo_id = "vatsal18/multi-lang_summay"
tok = AutoTokenizer.from_pretrained(repo_id)
mdl = AutoModelForSeq2SeqLM.from_pretrained(repo_id).eval()
text = "Paste any article (any supported language) here."
enc = tok(text, return_tensors="pt", truncation=True, max_length=1024)
with torch.no_grad():
out = mdl.generate(**enc, max_new_tokens=128, num_beams=4, length_penalty=0.8)
print(tok.decode(out[0], skip_special_tokens=True))
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="vatsal18/multi-lang_summay")