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 eval_results.json from philschmid/bart-large-cnn-samsum: direct link, hf CLI and curl.
- Browser
- Download file 454 Bytes
-
https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/eval_results.json
- Command line
-
hf download hf://philschmid/bart-large-cnn-samsum/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/philschmid/bart-large-cnn-samsum/resolve/main/eval_results.json
454 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_gen_len": 59.9829, | |
| "eval_loss": 1.3891159296035767, | |
| "eval_mem_cpu_alloc_delta": 78528783, | |
| "eval_mem_cpu_peaked_delta": 2605953, | |
| "eval_mem_gpu_alloc_delta": 0, | |
| "eval_mem_gpu_peaked_delta": 980464128, | |
| "eval_rouge1": 42.621, | |
| "eval_rouge2": 21.9825, | |
| "eval_rougeL": 33.034, | |
| "eval_rougeLsum": 39.6783, | |
| "eval_runtime": 29.301, | |
| "eval_samples": 818, | |
| "eval_samples_per_second": 27.917 | |
| } |