Update README.md

README.md

@@ -30,7 +30,7 @@ This model is based on a Llama2 model that was fine-tuned
       To run the model, the demo code is provided in demo.ipynb submitted.
       It is advised to use the pre-processing and post-processing functions (provided in demo.ipynb) along with the model for best results.
 
-- **Developed by:** Hei Chan and Mehedi Bari
+- **Developed by:** Hei Chan
 - **Language(s):** English
 - **Model type:** Supervised
 - **Model architecture:** Transformers
@@ -43,99 +43,4 @@ This model is based on a Llama2 model that was fine-tuned
 - **Repository:** https://huggingface.co/meta-llama/Llama-2-7b-hf
 - **Paper or documentation:** https://arxiv.org/abs/2307.09288
 
-## Training Details
-
-### Training Data
-
-<!-- This is a short stub of information on the training data that was used, and documentation related to data pre-processing or additional filtering (if applicable). -->
-
-30K pairs of texts drawn from emails, news articles and blog posts.
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Training Hyperparameters
-
-<!-- This is a summary of the values of hyperparameters used in training the model. -->
-
-
-      - learning_rate: 1e-05
-      - weight decay: 0.001
-      - train_batch_size: 2
-      - gradient accumulation steps: 4
-      - optimizer: paged_adamw_8bit
-      - LoRA r: 64
-      - LoRA alpha: 128
-      - LoRA dropout: 0.05
-      - RSLoRA: True
-      - max grad norm: 0.3
-      - eval_batch_size: 1
-      - num_epochs: 1
-
-#### Speeds, Sizes, Times
-
-<!-- This section provides information about how roughly how long it takes to train the model and the size of the resulting model. -->
-
-
-      - trained on: V100 16GB
-      - overall training time: 59 hours
-      - duration per training epoch: 59 hours
-      - model size: ~27GB
-      - LoRA adaptor size: 192 MB
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data & Metrics
-
-#### Testing Data
-
-<!-- This should describe any evaluation data used (e.g., the development/validation set provided). -->
-
-The development set provided, amounting to 6K pairs.
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used. -->
-
-
-      - Precision
-      - Recall
-      - F1-score
-      - Accuracy
-
-### Results
-
-
-      - Precision: 80.6%
-      - Recall: 80.4%
-      - F1 score: 80.3%
-      - Accuracy: 80.4%
-
-## Technical Specifications
-
-### Hardware
-
-
-      - Mode: Inference
-      - VRAM: at least 6 GB
-      - Storage: at least 30 GB,
-      - GPU: RTX3060
-
-### Software
-
-
-      - Transformers
-      - Pytorch
-      - bitesandbytes
-      - Accelerate
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-Any inputs (concatenation of two sequences plus prompt words) longer than
-      4096 subwords will be truncated by the model.