Update README.md

README.md

@@ -3,8 +3,6 @@ license: mit
 language:
 - en
 library_name: keras
-tags:
-- code
 ---
 # Model Card for Model ID
 
@@ -13,21 +11,18 @@ tags:
 This model is used to classify the user-intent for the Danswer project, visit https://github.com/danswer-ai/danswer.
 
 ## Model Details
-Trained multiclass classifier on top of distilbert-base-uncased
+Multiclass classifier on top of distilbert-base-uncased
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-Classifiers user intent into categories including:
+Classifies user intent of queries into categories including:
 0: Keyword Search
 1: Semantic Search
 2: Direct Question Answering
 
 
 - **Developed by:** [DanswerAI]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
 - **License:** [MIT]
 - **Finetuned from model [optional]:** [distilbert-base-uncased]
 
@@ -35,171 +30,55 @@ Classifiers user intent into categories including:
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/danswer-ai/danswer]
+- **Demo [optional]:** [Upcoming!]
 
 ## 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 model is intended to be used in the Danswer Question-Answering System
 
-<!-- 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]
+This model has a very small dataset maintained by DanswerAI. If interested, reach out to danswer.dev@gmail.com.
 
 ### 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.
+This model is intended to be used in the Danswer (QA System)
 
 ## 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 Data 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 Data 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. -->
+```
+from transformers import AutoTokenizer
+from transformers import TFDistilBertForSequenceClassification
+import tensorflow as tf
 
-[More Information Needed]
+model = TFDistilBertForSequenceClassification.from_pretrained("danswer/intent-model/")
+tokenizer = AutoTokenizer.from_pretrained("danswer/intent-model/")
 
-## More Information [optional]
+class_semantic_mapping = {
+        0: "Keyword Search",
+        1: "Semantic Search",
+        2: "Question Answer"
+    }
 
-[More Information Needed]
+# Get user input
+user_query = "How do I set up Danswer to run on my local environment?"
 
-## Model Card Authors [optional]
+# Encode the user input
+inputs = tokenizer(user_query, return_tensors="tf", truncation=True, padding=True)
 
-[More Information Needed]
+# Get model predictions
+predictions = model(inputs)[0]
 
-## Model Card Contact
+# Get predicted class
+predicted_class = tf.math.argmax(predictions, axis=-1)
 
-[More Information Needed]
+print(f"Predicted class: {class_semantic_mapping[int(predicted_class)]}")
+```