Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use ajaydahiya8822/ajayd1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajaydahiya8822/ajayd1 with Transformers:
# 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="ajaydahiya8822/ajayd1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ajaydahiya8822/ajayd1") model = AutoModelForSeq2SeqLM.from_pretrained("ajaydahiya8822/ajayd1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ajaydahiya8822/ajayd1: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/ajaydahiya8822/ajayd1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ajaydahiya8822/ajayd1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ajaydahiya8822/ajayd1/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 51cf744ca3a7a9610981ab86aa109d979fa51fdc2f2c77a4a766a6b3a9b65d7d
- Size of remote file:
- 1.63 GB
- SHA256:
- 9f453aa6edef4dba1893723b7313b57b06b60214442d308a8acc3baa9583dd7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.