Upload README.md

README.md

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-library_name: transformers
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+ This is a text classification model, fully fine-tuned from a ` allenai/scibert_scivocab_uncased`. It re-uses the main BERT model and fits an ordinal regression head on the `[CLS]` token. The model is fine-tuned on the certainty labels collected in [Wurl et al (2024): _Understanding Fine-Grained Distortions in Reports for Scientific Finding_](https://aclanthology.org/2024.findings-acl.369/). The authors originally collect certainty annotations from humans using a 4-point Likert Scale ranging from (1) Uncertain to (4) Certain. Because the resulting datasets suffer from severe class imbalance, we merge the classes (1) Uncertain and (2) Somewhat Uncertain. 
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+### Dataset Statistics
 
+There are 1330 examples in the training set and 334 in the test set. 
+Each example is a sentence long.
+Examples are filtered from the [copenlu/spiced](https://huggingface.co/datasets/copenlu/spiced) dataset to exhibit final score greater or equal than 4.
 
-## Model Details
+The original base rates are as follows:
 
-### Model Description
+| Class | Base Rate in Training set | Base Rate in Test set |
+| ----- | ------------------------- | --------------------- |
+| 0 - Uncertain | 5.5970 | 7.1856 | 
+| 1 - Somewhat Uncertain | 15.2985 |  17.6647 |
+| 2 - Somewhat Certain  | 32.3881 | 33.2335 | 
+| 3 - Certain | 46.7164 | 41.9162 |
 
-<!-- Provide a longer summary of what this model is. -->
+After combining classes 0 and 1, we obtain the base rates below. Note that this mimicks the procedure adopted in the original paper.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+| Class | Base Rate in Training set | Base Rate in Test set |
+| ----- | ------------------------- | --------------------- |
+| 0 - Uncertain | 20.8955 | 24.8503 | 
+| 1 - Somewhat Certain  | 32.3881 | 33.2335 | 
+| 2 - Certain | 46.7164 | 41.9162 |
 
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-### Model Sources [optional]
 
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+### Hyperparameter Optimization
 
-## Uses
+The published model represents one of the 29 models different configurations. The selected model maximizes Quadratic Weighted Kappa (implemented using [cohen_kappa with quadratic weights](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cohen_kappa_score.html)), which is better adapted to ordinal problems, such as ordinal scales. Under this metric, a random model would score 0. We adopt this metric as opposed to accuracy or macro F1 to address class imbalances. 
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Here is the classification report and test set metrics:
 
-### Direct Use
+```
+17:44:36  INFO        test  loss=0.9565  acc=0.578  QWK=0.5004
+17:44:36  INFO      
+              precision    recall  f1-score   support
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+           0       0.58      0.51      0.54        83
+           1       0.47      0.46      0.46       111
+           2       0.65      0.71      0.68       140
 
-[More Information Needed]
+    accuracy                           0.58       334
+   macro avg       0.57      0.56      0.56       334
+weighted avg       0.57      0.58      0.57       334
+```
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+We conduct a hyperparameter sweep of the following hyperp
 
-[More Information Needed]
+- Freeze / Unfreeze 
+- LR: 1e-6 through 1e-3
+- Batch Size: 16, 32
+- Hidden Size Dimensions: 256, 128
+- Warmup Ratio: 0.05, 0.1, 0.2, 0.3
+- Epochs 30 (with patience)
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+## Usage 
+ 
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-## Bias, Risks, and Limitations
+model = AutoModelForSequenceClassification.from_pretrained("cbelem/scibert-certainty-ordinal", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("cbelem/scibert-certainty-ordinal", trust_remote_code=True)
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
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-
-### 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.
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-## Training Details
-
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- 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).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-## Citation [optional]
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-## Glossary [optional]
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