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license: apache-2.0
language:
- en
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-ranking
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a text Reranker model to score if a text is kindergarten-teacher style.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Miao025
- **Model type:** Text Reranking
- **Model size:** 67M params
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model [optional]:** distilbert/distilbert-base-uncased
- **Demo:** [A fine-tuned AI KinderChatbot using this Rerank model](https://huggingface.co/spaces/Miao025/qwen-kinderchatbot)
## Useage
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
```
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and reranker model (Note that you can also download the models and load with local path.)
tokenizer_reward = AutoTokenizer.from_pretrained("Miao025/Qwen-KinderChatbot-Reward")
reward_model = AutoModelForSequenceClassification.from_pretrained("Miao025/Qwen-KinderChatbot-Reward")
# For each prompt-response pair, get the score
inputs = tokenizer_reward(prompt, response, return_tensors="pt", truncation=True)
with torch.no_grad():
logits = reward_model(**inputs).logits
score = torch.softmax(logits, dim=-1)[0,1].item()
```
## Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[Training Dataset Card](to be add)
Training process can be found on [Github](https://github.com/Miao025/KinderChatbot).
## Contact
For any questions, please contact the author yinmiao025@gmail.com |