Update README.md

README.md

@@ -1,12 +1,19 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
+base_model:
+- allenai/scibert_scivocab_uncased
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+This is a text classification model. 
+It was fine-tuned to predict certainty ratings of scientific findings using a classification loss and a ranking loss. 
+We fine-tuned an allenai/scibert_scivocab_uncased on the dataset made available by [Wurl et al (2024): Understanding Fine-Grained Distortions in Reports for Scientific Finding.](https://aclanthology.org/2024.findings-acl.369/).
 
 
 ## Model Details
@@ -17,92 +24,62 @@ tags: []
 
 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):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Researchers at UCI with the goal of obtaining a reliable certainty scoring function.
+- **Model type:** BERT
+- **Language(s) (NLP):** English
+- **Finetuned from model:** allenai/scibert_scivocab_uncased
 
 ## 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]
+The model is meant to be used for estimating certainty scores. Because it is trained on sentence-level academic findings, we suspect its reliability to be restricted to this domain. 
+The original dataset had only moderate inter-annotator agreement (spearman correlation coefficient of 0.44), which suggests that predicting certainty scores is difficult even for humans. 
+We recommend users of this model to validate that the model behaves as intended in a small portion of the data of interest before scaling evaluations. 
+We also note that the per-class F1 scores ranged between (0.48-0.70), which reflects once again the difficulty in learning clear class boundaries.
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use the code below to get started with the model.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-## How to Get Started with the Model
+tokenizer = AutoTokenizer.from_pretrained("Cbelem/scibert-certainty-classif")
+model = AutoModelForSequenceClassification.from_pretrained("Cbelem/scibert-certainty-classif")
+model.eval()
 
-Use the code below to get started with the model.
+texts = [
+    "Compared with controls, taxi drivers had greater grey matter volume in the posterior hippocampi (Maguire et al.",
+    "The study described in this paper focuses on gaze, but similar approaches can be used to understand the effects of other interactions that contribute to patient outcomes such as emotion.",
+    '""The initial findings could have been explained by a correlation, that people with big hippocampi become taxi drivers,"" he says.',
+    "We are less sure about a possible explanation for lower acceptance for mobile phone behaviors among professionals in the West.",
+]
 
-[More Information Needed]
+inputs_ids = tokenizer(texts, return_tensors="pt")
+model(**inputs_ids)
+```
 
 ## 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]
+TBD
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+TBD
 
 #### Preprocessing [optional]
 
-[More Information Needed]
+TBD
 
 
 #### 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]
+- **Training regime:** fp32
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
 ### Testing Data, Factors & Metrics
 
@@ -120,41 +97,42 @@ Use the code below to get started with the model.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+TBD 
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
+```
+  "train/learning_rate": 6.869747470432602e-7,
+  "train/loss": 0.562,
+  "train/global_step": 3000,
+  "eval/qwk": 0.5507,
+  "eval/loss": 0.9391,
+  "eval/accuracy": 0.6078,
+  "eval/balanced_accuracy": 0.3980,
+  "eval/f1_macro": 0.6006,
+  "eval/f1_class_0": 0.6211,
+  "eval/f1_class_1": 0.4932,
+  "eval/f1_class_2": 0.6875,
+  "eval/precision_macro": 0.6033,
+  "eval/precision_class_0": 0.6410,
+  "eval/precision_class_1": 0.5,
+  "eval/precision_class_2": 0.6689,
+  "eval/recall_macro": 0.5987,
+  "eval/recall_class_0": 0.6024,
+  "eval/recall_class_1": 0.4865,
+  "eval/recall_class_2": 0.7071,
+  "train_steps_per_second": 6.532,
+```
 
 
-## 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).
+#### Summary
 
-- **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]
+TBD
 
 ### Compute Infrastructure
 
@@ -166,34 +144,18 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### 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]
-
-**APA:**
+Transformers, Pytorch, Wandb for running the hyperparameter sweep
 
-[More Information Needed]
+## Citation
 
-## Glossary [optional]
+TBD
 
-<!-- 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]
+## Model Card Authors
 
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+Catarina Belem (Cbelem)
 
 ## Model Card Contact
 
-[More Information Needed]
+For more information contact cbelem@uci.edu.