README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** `en`
- **License:** mit
- **Finetuned from model [optional]:** Qwen/Qwen2.5-14B-Instruct

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

```python
import transformers
import torch
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import (
    pipeline,
    AutoModelForCausalLM,
    PreTrainedTokenizer
)
from typing import (
    Dict,
    Callable,
    Tuple,
    List,
)
from src.pipelines.level_to_score_pipeline import LevelToScorePipeline
from src.rank_dicts import SingleLabelRankDict
from src.chat_templates import UNLITemplate


model = transformers.AutoModelForCausalLM.from_pretrained(
    "Zhengping/conditional-probability-regression",
    torch_dtype="auto",
    attn_implementation="flash_attention_2",
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
    "Zhengping/conditional-probability-regression",
)

rank_dict = SingleLabelRankDict.from_tokenizer(tokenizer)

PIPELINE_REGISTRY.register_pipeline(
    "level-to-score",
    pipeline_class=LevelToScorePipeline,
    pt_model=AutoModelForCausalLM
)

# This allows fine-grained labeling, the greedy decoding gives a coarse score,
# one can also attach their own level-to-score function to the pipeline, e.g. using UNLI
# label transformation to get it more binarized
def _level_to_score_func(
    logits: Tuple[torch.FloatTensor],
    tokenizer: PreTrainedTokenizer
) -> Tuple[List[float], List[float]]:
    """ """
    logits = logits[0]
    num_labels = len(rank_dict)
    considering_ids = tokenizer.convert_tokens_to_ids([f" <|label_level_{i}|>" for i in range(num_labels)])
    selective_logits = torch.index_select(logits, 1, torch.tensor(considering_ids, device=logits.device))
    step_size = 1 / num_labels
    expectation = torch.tensor([[i * step_size + 1 / 2 * step_size for i in range(num_labels)]], device=selective_logits.device)
    scores = torch.softmax(selective_logits, dim=-1) @ expectation.T
    scores = scores.squeeze(-1).tolist()
    return scores, selective_logits.tolist()

pipe = pipeline(
    "level-to-score",
    model=model,
    max_new_tokens=2,
    tokenizer=tokenizer,
    device=0,
    level_to_score_func=_level_to_score_func,
    torch_dtype=torch.bfloat16,
)

template = UNLITemplate()

premise = "Sam is sleeping."
hypothesis = "Sam is awake."

inputs = template.get_prompt_template(premise=premise, hypothesis=hypothesis) +\
    template.get_completion_template(is_completion=True)
    
result = pipe(inputs)
print(result)
```

See our code repo for the definition of the scoring pipeline and templates.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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