File size: 2,876 Bytes
b20270a bdd2bf5 169bc0e 26db169 169bc0e 26db169 169bc0e 26db169 169bc0e 26db169 169bc0e 26db169 169bc0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
language:
- en
base_model:
- answerdotai/ModernBERT-base
tags:
- modernbert
- summarization
- 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [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]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
|