Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
Generated from Trainer
Instructions to use Sidharthkr/InstructTweetSummarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sidharthkr/InstructTweetSummarizer 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="Sidharthkr/InstructTweetSummarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Sidharthkr/InstructTweetSummarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Sidharthkr/InstructTweetSummarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba697a5b32fc01bf166486c23ace88b95a34e18de3634684f9c479ccbfc66a52
- Size of remote file:
- 1.63 GB
- SHA256:
- a974372ecd23751fbe71ad5e8ec22179b98d2a947233343705c2db6f5edd8d28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.