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Here is a README for the urdu_ghazals model: Urdu Ghazals Model This model generates Urdu ghazals in response to English translated prompts. The model was trained and fine-tuned on a dataset of 9000 samples of Urdu ghazals, including the famous ghazal by Mirza Ghalib: hazāroñ ḳhvāhisheñ aisī ki har ḳhvāhish pe dam nikle bahut nikle mire armān lekin phir bhī kam nikle Dare kyuuñ merā qātil kyā rahegā us kī gardan par vo ḳhuuñ jo chashm-e-tar se umr bhar yuuñ dam-ba-dam nikle nikalnā ḳhuld se aadam kā sunte aa.e haiñ lekin bahut be-ābrū ho kar tire kūche se ham nikle bharam khul jaa.e zālim tere qāmat kī darāzī kā agar is turra-e-pur-pech-o-ḳham kā pech-o-ḳham nikle magar likhvā.e koī us ko ḳhat to ham se likhvā.e huī sub.h aur ghar se kaan par rakh kar qalam nikle huī is daur meñ mansūb mujh se bāda-ashāmī phir aayā vo zamāna jo jahāñ meñ jām-e-jam nikle huī jin se tavaqqo ḳhastagī kī daad paane kī vo ham se bhī ziyāda ḳhasta-e-teġh-e-sitam nikle mohabbat meñ nahīñ hai farq jiine aur marne kā usī ko dekh kar jiite haiñ jis kāfir pe dam nikle kahāñ mai-ḳhāne kā darvāza 'ġhālib' aur kahāñ vaa.iz par itnā jānte haiñ kal vo jaatā thā ki ham nikle Copy A good prompt for this model would be an English translated line from an Urdu ghazal, such as “kuch keha tha aap se”. The model will generate an Urdu ghazal in response to the prompt. We hope you enjoy using this model to generate beautiful Urdu ghazals! 😊
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Model trained on 55 +8 epochs
rng_state.pth
Detected Pickle imports (7)
- "numpy.dtype",
- "collections.OrderedDict",
- "numpy.core.multiarray._reconstruct",
- "numpy.ndarray",
- "torch.ByteStorage",
- "_codecs.encode",
- "torch._utils._rebuild_tensor_v2"
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training_args.bin
Detected Pickle imports (8)
- "accelerate.utils.dataclasses.DistributedType",
- "accelerate.state.PartialState",
- "transformers.trainer_utils.HubStrategy",
- "transformers.training_args.TrainingArguments",
- "transformers.training_args.OptimizerNames",
- "transformers.trainer_utils.IntervalStrategy",
- "torch.device",
- "transformers.trainer_utils.SchedulerType"
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