dirtycomputer/weibo_senti_100k
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How to use wsqstar/GISchat-weibo-100k-fine-tuned-bert with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="wsqstar/GISchat-weibo-100k-fine-tuned-bert") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wsqstar/GISchat-weibo-100k-fine-tuned-bert")
model = AutoModelForSequenceClassification.from_pretrained("wsqstar/GISchat-weibo-100k-fine-tuned-bert")This model is a fine-tuned version of bert-base-chinese on weibo-100k dataset.
Github repo: https://github.com/GISChat/Fine-tune-bert
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 | Accuracy |
|---|---|---|---|---|
| No log | 0.08 | 100 | 0.6573 | 0.606 |
| 0.647 | 0.16 | 200 | 0.2447 | 0.9507 |
| 0.647 | 0.24 | 300 | 0.0914 | 0.9807 |
| 0.1276 | 0.32 | 400 | 0.0609 | 0.9843 |
| 0.1276 | 0.4 | 500 | 0.0607 | 0.9843 |
| 0.0921 | 0.48 | 600 | 0.1053 | 0.98 |
| 0.0921 | 0.56 | 700 | 0.0487 | 0.9853 |
| 0.0885 | 0.64 | 800 | 0.0523 | 0.9853 |
| 0.0885 | 0.72 | 900 | 0.0484 | 0.986 |
| 0.0579 | 0.8 | 1000 | 0.0549 | 0.985 |
| 0.0579 | 0.88 | 1100 | 0.0495 | 0.9867 |
| 0.0507 | 0.96 | 1200 | 0.0458 | 0.9867 |
Base model
google-bert/bert-base-chinese