Question Answering
Transformers
PyTorch
Portuguese
t5
text2text-generation
bert
bert-base
text-generation-inference
Instructions to use GuiSales404/e10_lr0.0001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GuiSales404/e10_lr0.0001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="GuiSales404/e10_lr0.0001")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("GuiSales404/e10_lr0.0001") model = AutoModelForSeq2SeqLM.from_pretrained("GuiSales404/e10_lr0.0001", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d9593be90b1ff03193f37e3f79854d62c0fe516ec23beda84fcabeeb54bff3e
- Size of remote file:
- 3.57 kB
- SHA256:
- 78ebb3e327f28333f47c89943abd0ab69799db7dd6c123cf07bbc7f04e1617dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.