Update README.md

README.md

@@ -17,9 +17,9 @@
 
 
 - **Developed by:** Declan Bracken, Armando Ordorica, Michael Santorelli, Paul Zhou
-- **Model type:** [Transformer]
-- **Language(s) (NLP):** [English]
-- **Finetuned from model [optional]:** [BERT_base_uncased]
+- **Model type:** Transformer
+- **Language(s) (NLP):** English
+- **Finetuned from model:** BERT_base_uncased
 
 ### Model Sources [optional]
 
@@ -35,163 +35,39 @@
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Create a custom class to load in the model, the label encoder, and the BERT tokenizer used for training (bert-base-uncased) as below.
+use the tokenizer to tokenize any input string you'd like, then pass it through the model to get outputs.
 
-[More Information Needed]
+class BERTClassifier:
+    def __init__(self, model_identifier):
+        # Load the tokenizer from bert base uncased
+        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 
-### Downstream Use [optional]
+        # Load the config
+        config = AutoConfig.from_pretrained(model_identifier)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+        # Load the model
+        self.model = BertForSequenceClassification.from_pretrained(model_identifier, config=config)
+        self.model.eval()  # Set the model to evaluation mode
 
-[More Information Needed]
+        # Load the label encoder
+        encoder_url = f'https://huggingface.co/{model_identifier}/resolve/main/model_encoder.pkl'
+        self.labels = pickle.loads(requests.get(encoder_url).content)
 
-### Out-of-Scope Use
+    def predict_category(self, text):
+        # Tokenize the text
+        inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+        # Predict
+        with torch.no_grad():
+            outputs = self.model(**inputs)
 
-[More Information Needed]
+        # Get the prediction index
+        prediction_idx = torch.argmax(outputs.logits, dim=1).item()
 
-## Bias, Risks, and Limitations
+        # Decode the prediction index to get the label
+        prediction_label = self.labels[prediction_idx]  # Use indexing for a NumPy array
 
-<!-- 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
-
-[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]
-
-**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]
+        return prediction_label