Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use philschmid/bart-large-cnn-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/bart-large-cnn-samsum 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="philschmid/bart-large-cnn-samsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("philschmid/bart-large-cnn-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("philschmid/bart-large-cnn-samsum", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download test_results.json from philschmid/bart-large-cnn-samsum: direct link, hf CLI and curl.
- Browser
- Download file 438 Bytes
-
https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/test_results.json
- Command line
-
hf download hf://philschmid/bart-large-cnn-samsum/test_results.json
-
curl -L -o test_results.json https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/test_results.json
438 Bytes
| { | |
| "test_gen_len": 60.0757, | |
| "test_loss": 1.4224162101745605, | |
| "test_mem_cpu_alloc_delta": 82666029, | |
| "test_mem_cpu_peaked_delta": 2572070, | |
| "test_mem_gpu_alloc_delta": 0, | |
| "test_mem_gpu_peaked_delta": 1441433088, | |
| "test_rouge1": 41.3174, | |
| "test_rouge2": 20.8716, | |
| "test_rougeL": 32.1337, | |
| "test_rougeLsum": 38.4149, | |
| "test_runtime": 34.32, | |
| "test_samples": 819, | |
| "test_samples_per_second": 23.864 | |
| } |