Summarization
Transformers
PyTorch
Safetensors
Hebrew
t5
text2text-generation
text-generation-inference
Instructions to use imvladikon/het5_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imvladikon/het5_summarization 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="imvladikon/het5_summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("imvladikon/het5_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("imvladikon/het5_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from imvladikon/het5_summarization: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/imvladikon/het5_summarization/resolve/main/train_results.json
- Command line
-
hf download hf://imvladikon/het5_summarization/train_results.json
-
curl -L -o train_results.json https://huggingface.co/imvladikon/het5_summarization/resolve/main/train_results.json
194 Bytes
| { | |
| "epoch": 1.0, | |
| "train_loss": 1.77077664958091, | |
| "train_runtime": 65437.7493, | |
| "train_samples": 287113, | |
| "train_samples_per_second": 4.388, | |
| "train_steps_per_second": 2.194 | |
| } |