TrongNghiaCS/vietnews_clean
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How to use mrzaizai2k/my_fine_tuned_t5_small_model 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="mrzaizai2k/my_fine_tuned_t5_small_model") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mrzaizai2k/my_fine_tuned_t5_small_model")
model = AutoModelForSeq2SeqLM.from_pretrained("mrzaizai2k/my_fine_tuned_t5_small_model", device_map="auto")This model is a fine-tuned version of huggingface-course/mt5-finetuned-amazon-en-es on the None 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 |
|---|---|---|---|---|---|---|---|
| 4.428 | 1.0 | 13 | 2.8216 | 19.1382 | 5.389 | 15.1855 | 15.1893 |
| 4.299 | 2.0 | 26 | 2.6894 | 19.6236 | 5.4931 | 15.4001 | 15.5122 |
| 4.0999 | 3.0 | 39 | 2.5552 | 22.2435 | 6.6021 | 17.2465 | 17.4679 |
| 3.8541 | 4.0 | 52 | 2.4394 | 23.3478 | 7.1578 | 18.4112 | 18.6265 |
| 3.6964 | 5.0 | 65 | 2.3568 | 25.092 | 7.4595 | 19.3078 | 19.5198 |
| 3.5481 | 6.0 | 78 | 2.3018 | 26.0743 | 8.0058 | 19.9644 | 20.2044 |
| 3.2977 | 7.0 | 91 | 2.2207 | 26.8852 | 7.9185 | 20.5406 | 20.7554 |
| 3.2768 | 8.0 | 104 | 2.1832 | 26.3885 | 7.9643 | 20.3863 | 20.6338 |
| 3.2047 | 9.0 | 117 | 2.1403 | 26.598 | 7.883 | 20.3585 | 20.4868 |
| 3.0813 | 10.0 | 130 | 2.1069 | 26.742 | 7.9622 | 20.4423 | 20.7426 |
Base model
google/mt5-small