Translation
Transformers
Safetensors
Telugu
English
t5
text2text-generation
indic-nlp
telugu
mt5
hybrid-training
full-finetune
Eval Results (legacy)
text-generation-inference
Instructions to use ManiKumarAdapala/mt5-telugu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManiKumarAdapala/mt5-telugu with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="ManiKumarAdapala/mt5-telugu")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ManiKumarAdapala/mt5-telugu") model = AutoModelForSeq2SeqLM.from_pretrained("ManiKumarAdapala/mt5-telugu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1dadd3c7b4c6a50145de7d09ef504cda57a98a9254afee35d4a80ef47fb8fc1
- Size of remote file:
- 600 MB
- SHA256:
- 503739ad4bb31a6203b34b7b8b6f5648e4985ebcb89f9e4be71011374a906117
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.