Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slauw87/bart_summarisation 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="slauw87/bart_summarisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_gen_len": 59.6381, | |
| "eval_loss": 1.3911350965499878, | |
| "eval_mem_cpu_alloc_delta": 83144704, | |
| "eval_mem_cpu_peaked_delta": 454656, | |
| "eval_mem_gpu_alloc_delta": 0, | |
| "eval_mem_gpu_peaked_delta": 1348802048, | |
| "eval_rouge1": 43.2111, | |
| "eval_rouge2": 22.3519, | |
| "eval_rougeL": 33.3153, | |
| "eval_rougeLsum": 40.0527, | |
| "eval_runtime": 51.2073, | |
| "eval_samples": 818, | |
| "eval_samples_per_second": 15.974 | |
| } |