williambrach
/

ModernBERT-base-cos

@@ -9,19 +9,20 @@ tags:
 - regression
 - qna
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
@@ -33,138 +34,52 @@ This modelcard aims to be a base template for new models. It has been generated
 ### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### 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
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## 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
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
 #### Hardware
@@ -182,24 +97,3 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 - regression
 - qna
 ---
+# Model Card for ModernBERT-base-cos
+ModernBERT-base-cos is a ModernBERT-based sequence classification model specifically fine-tuned to assess the quality of summaries in a QnA context. This model is designed to evaluate how well a generated summary captures essential information needed for question-answering tasks as part of research on the "chain of summaries" approach.
 ## Model Details
 ### Model Description
+This model evaluates the quality and completeness of summaries by providing a quality score. It helps determine whether a summary adequately captures the information needed for downstream QnA tasks, making it useful for:
+- Researchers working on summarization evaluation
+- QnA pipeline optimization
+- Educational applications requiring assessment of student-generated summaries
+- Content creation platforms where summary quality is important
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 ### Model Sources [optional]
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Load model and tokenizer
+model_id = "williambrach/ModernBERT-base-cos"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id).to("cuda")
+def summary_score(
+    tokenizer,
+    summaries: list[str],
+    device: str = "cuda",
+    return_tensor: bool = True,
+):
+    inputs = tokenizer(
+        summaries, return_tensors="pt", padding=True, truncation=True
+    ).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = torch.sigmoid(outputs["logits"])
+        if return_tensor:
+            logits = logits
+        else:
+            logits = logits.cpu().numpy().tolist()
+    return logits
+# Example
+texts = [
+    "test",
+    "Michael Jackson was a famous singer and dancer.",
+    "Michael Jackson was a famous singer.",
+    "Michael Jackson was a famous dancer.",
+]
+scores = summary_score(tokenizer, texts, return_tensor=False)
+print(scores)
+```
 #### Hardware
 [More Information Needed]