Instructions to use twn39/bert-base-chinese-finetune-dianping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twn39/bert-base-chinese-finetune-dianping with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="twn39/bert-base-chinese-finetune-dianping")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("twn39/bert-base-chinese-finetune-dianping") model = AutoModelForSequenceClassification.from_pretrained("twn39/bert-base-chinese-finetune-dianping", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- zh
|
| 5 |
+
metrics:
|
| 6 |
+
- accuracy
|
| 7 |
+
base_model:
|
| 8 |
+
- google-bert/bert-base-chinese
|
| 9 |
+
pipeline_tag: text-classification
|
| 10 |
+
library_name: transformers
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
最终评估结果: {'eval_loss': 0.6101630330085754, 'eval_accuracy': 0.7496291901512904, 'eval_runtime': 25.5189, 'eval_samples_per_second': 264.196, 'eval_steps_per_second': 16.537, 'epoch': 3.0}
|