docs: update readme

README.md

@@ -1,199 +1,122 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- question-answering
+- distilbert
+- squad
+- fine-tuned
+datasets:
+- squad
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for harpertoken/harpertokenConvAI-finetuned
 
+This model is a fine-tuned version of harpertoken/harpertokenConvAI, a DistilBERT-based question answering model, trained on a subset of the SQuAD dataset.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is a fine-tuned question answering model based on DistilBERT, optimized for extractive QA tasks. It has been trained on a small subset of the SQuAD dataset to demonstrate fine-tuning capabilities in a CI environment.
 
-- **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]
+- **Developed by:** bniladridas
+- **Model type:** DistilBERT for Question Answering
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** harpertoken/harpertokenConvAI
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- 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/bniladridas/harpertoken
 
 ## 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]
+This model can be used directly for question answering on passages similar to SQuAD. Provide a question and context, and it will predict the answer span.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+Can be further fine-tuned on domain-specific data for improved performance.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Not suitable for non-English text, generative tasks, or domains outside of factual QA.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Trained on a limited SQuAD subset, may exhibit biases from the dataset. Performance may degrade on out-of-domain questions.
 
 ### 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.
+Evaluate on your specific data and consider additional fine-tuning for production use.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import pipeline
 
-[More Information Needed]
+qa = pipeline("question-answering", model="harpertoken/harpertokenConvAI-finetuned")
+result = qa(question="What is the capital of France?", context="France is a country in Europe. Paris is the capital.")
+print(result)
+```
 
 ## 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]
+Subset of SQuAD 1.1 dataset (approximately 1000 examples).
 
 ### 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]
+- **Training regime:** fp32
+- **Epochs:** 1
+- **Batch size:** 1
+- **Learning rate:** 2e-5
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+Trained in CI environment, minimal time due to small dataset.
 
 ## 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]
+SQuAD validation set subset.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+F1 score, Exact Match.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Basic evaluation on sample questions.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Minimal impact due to small-scale training in CI.
 
-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:** GitHub Actions runners
+- **Carbon Emitted:** Negligible
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+DistilBERT encoder with QA head for span prediction.
 
 ### 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]
+GitHub Actions Ubuntu runners.
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+If you use this model, please cite the original DistilBERT and SQuAD papers.
 
 ## Model Card Contact
 
-[More Information Needed]
+bniladridas