Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use yeye776/OndeviceAI-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeye776/OndeviceAI-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yeye776/OndeviceAI-base") model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/OndeviceAI-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -15,19 +15,31 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 15 |
|
| 16 |
This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) on the None dataset.
|
| 17 |
|
| 18 |
-
##
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 31 |
|
| 32 |
### Training hyperparameters
|
| 33 |
|
|
|
|
| 15 |
|
| 16 |
This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) on the None dataset.
|
| 17 |
|
| 18 |
+
## How to use
|
| 19 |
+
```python
|
| 20 |
+
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
|
| 21 |
+
from typing import List
|
| 22 |
+
|
| 23 |
+
tokenizer = AutoTokenizer.from_pretrained("yeye776/OndeviceAI-base")
|
| 24 |
+
model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/OndeviceAI-base")
|
| 25 |
+
|
| 26 |
+
prompt = "분류 및 인식해줘 :"
|
| 27 |
+
def prepare_input(question: str):
|
| 28 |
+
inputs = f"{prompt} {question}"
|
| 29 |
+
input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
|
| 30 |
+
return input_ids
|
| 31 |
+
|
| 32 |
+
def inference(question: str) -> str:
|
| 33 |
+
input_data = prepare_input(question=question)
|
| 34 |
+
input_data = input_data.to(model.device)
|
| 35 |
+
outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=1024)
|
| 36 |
+
|
| 37 |
+
result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
|
| 38 |
+
|
| 39 |
+
return result
|
| 40 |
+
|
| 41 |
+
inference("안방 조명 켜줘")
|
| 42 |
+
```
|
| 43 |
|
| 44 |
### Training hyperparameters
|
| 45 |
|