armanc/scientific_papers
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How to use GerardoCevallos/mt5-small-finetuned-amazon-en-es 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="GerardoCevallos/mt5-small-finetuned-amazon-en-es") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("GerardoCevallos/mt5-small-finetuned-amazon-en-es")
model = AutoModelForSeq2SeqLM.from_pretrained("GerardoCevallos/mt5-small-finetuned-amazon-en-es", device_map="auto")This model is a fine-tuned version of google/mt5-small on the scientific_papers 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 |
|---|---|---|---|---|---|---|---|
| 17.5935 | 1.0 | 50 | 8.3763 | 1.0909 | 0.7547 | 1.0909 | 1.0909 |
| 10.9464 | 2.0 | 100 | 4.7599 | 3.5415 | 0.0 | 2.927 | 2.547 |
| 6.5498 | 3.0 | 150 | 3.6772 | 6.7136 | 1.8214 | 5.2193 | 5.925 |
| 6.0161 | 4.0 | 200 | 3.5188 | 7.0145 | 1.8214 | 5.8355 | 6.2468 |
Base model
google/mt5-small