TrongNghiaCS/vietnews_clean
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How to use mrzaizai2k/vietnamese_mt5_summary_model_2 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/vietnamese_mt5_summary_model_2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mrzaizai2k/vietnamese_mt5_summary_model_2")
model = AutoModelForSeq2SeqLM.from_pretrained("mrzaizai2k/vietnamese_mt5_summary_model_2", device_map="auto")This model was trained from scratch 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 |
|---|---|---|---|---|---|---|---|
| 1.5757 | 1.0 | 313 | 1.4225 | 57.3823 | 23.8437 | 35.6288 | 37.1079 |
| 1.5452 | 2.0 | 626 | 1.4219 | 56.809 | 23.4657 | 35.0977 | 36.7606 |
| 1.5074 | 3.0 | 939 | 1.4122 | 55.9095 | 23.0128 | 35.0274 | 36.4447 |
| 1.4701 | 4.0 | 1252 | 1.4256 | 56.621 | 23.1876 | 35.1323 | 36.5518 |
| 1.431 | 5.0 | 1565 | 1.4381 | 57.2067 | 23.6087 | 35.1239 | 36.7421 |
| 1.3929 | 6.0 | 1878 | 1.4338 | 57.1248 | 23.9446 | 35.3666 | 36.9974 |
| 1.3558 | 7.0 | 2191 | 1.4727 | 57.0482 | 23.1001 | 34.8187 | 36.1817 |
| 1.3197 | 8.0 | 2504 | 1.4928 | 56.0409 | 23.1702 | 35.4414 | 36.858 |
| 1.2861 | 9.0 | 2817 | 1.4917 | 57.1416 | 23.7555 | 35.5747 | 36.9418 |
| 1.2367 | 10.0 | 3130 | 1.4813 | 57.2618 | 23.5562 | 35.4717 | 37.1259 |