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google/mt5-base1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_id = "tasal9/ZamAI-mT5-Pashto"
4model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
5tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
6
7prompt = "سلام دې وي! تاسو څنګه یاست؟"
8inputs = tokenizer(prompt, return_tensors="pt")
9outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
10print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Metric | Value | Description |
|---|---|---|
| bleu | TBD | Add measured value |
| rouge | TBD | Add measured value |
Update this table with your measured results and link to the evaluation script/notebook.
1@misc{zamai_pashto,
2 title = {{ZamAI mT5 Pashto}},
3 author = {ZamAI / Yaqoob Tasal},
4 year = {2024},
5 howpublished = {\url{https://huggingface.co/tasal9/ZamAI-mT5-Pashto}}
6}