EdinburghNLP/xsum
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How to use sunsvrv/bart-base-facebook 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="sunsvrv/bart-base-facebook") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sunsvrv/bart-base-facebook")
model = AutoModelForSeq2SeqLM.from_pretrained("sunsvrv/bart-base-facebook")# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sunsvrv/bart-base-facebook")
model = AutoModelForSeq2SeqLM.from_pretrained("sunsvrv/bart-base-facebook")This model is a fine-tuned version of facebook/bart-base on the xsum dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.505 | 1.0 | 1359 | 2.1048 | 0.6788 | 0.3022 | 0.2843 | 0.6497 |
| 2.0216 | 2.0 | 2718 | 2.1010 | 0.7022 | 0.3182 | 0.2974 | 0.672 |
| 1.7088 | 3.0 | 4077 | 2.1228 | 0.7048 | 0.3214 | 0.2968 | 0.6722 |
| 1.4778 | 4.0 | 5436 | 2.1655 | 0.7117 | 0.325 | 0.2984 | 0.6786 |
| 1.3161 | 5.0 | 6795 | 2.1877 | 0.7146 | 0.3305 | 0.2988 | 0.6822 |
# 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="sunsvrv/bart-base-facebook")