Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 license: mit
 license_link: >-
-  https://huggingface.co/soulhq-ai/phi-2-insurance_qa-sft-lora/resolve/main/LICENSE
+  https://huggingface.co/deccan-ai/phi-2-insurance_qa-sft-lora/resolve/main/LICENSE
 language:
 - en
 pipeline_tag: text-generation
@@ -14,22 +14,24 @@ tags:
 - transformers
 - qa
 - sft
-
 datasets:
-- soulhq-ai/insuranceQA-v2
+- deccan-ai/insuranceQA-v2
 widget:
-- text: "### Instruction: What is the difference between health and life insurance?\n#### Response: "
-- text: "### Instruction: Does Homeowners Insurance Cover Death Of Owner?\n#### Response: "
-
+- text: |-
+    ### Instruction: What is the difference between health and life insurance?
+    #### Response: 
+- text: |-
+    ### Instruction: Does Homeowners Insurance Cover Death Of Owner?
+    #### Response: 
 ---
 ## Model Summary
 This model builds on the architecture of  <a href="https://huggingface.com/microsoft/phi-2">Microsoft's Phi-2</a>, incorporating the LoRA [[1]](#1) paradigm for supervised fine-tuning on a high quality question answering dataset in the insurance domain.
-Thus, `soulhq-ai/phi-2-insurance_qa-sft-lora` serves as a text generation model capable of answering questions around insurance.
+Thus, `deccan-ai/phi-2-insurance_qa-sft-lora` serves as a text generation model capable of answering questions around insurance.
 
 ## Dataset
 We utilise the InsuranceQA dataset [[2]](#2), which comprises 27.96K QA pairs related to the insurance domain.
 The content of this dataset consists of questions from real world users, the answers with high quality were composed by insurance professionals with deep domain knowledge.
-Since the dataset isn't available in a readable format on the web, we make it available on huggingface in a `jsonl` format, at <a href="https://huggingface.com/datasets/soulhq-ai/insuranceQA-v2">soulhq-ai/insuranceQA-v2</a>.
+Since the dataset isn't available in a readable format on the web, we make it available on huggingface in a `jsonl` format, at <a href="https://huggingface.com/datasets/deccan-ai/insuranceQA-v2">deccan-ai/insuranceQA-v2</a>.
 
 
 ## Usage 
@@ -61,8 +63,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 torch.set_default_device("cuda")
 
-model = AutoModelForCausalLM.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", torch_dtype="auto", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deccan-ai/phi-2-insurance_qa-sft-lora", torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("deccan-ai/phi-2-insurance_qa-sft-lora", trust_remote_code=True)
 
 inputs = tokenizer('''### Instruction: What Does Basic Homeowners Insurance Cover?\n### Response: ''', return_tensors="pt", return_attention_mask=False)
 
@@ -90,7 +92,7 @@ print(text)
 ## Evaluation
 Coming Soon!
  
-## Limitations of `soulhq-ai/phi-2-insurance_qa-sft-lora`
+## Limitations of `deccan-ai/phi-2-insurance_qa-sft-lora`
 * Generate Inaccurate Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
 * Unreliable Responses to Instruction: It may struggle or fail to adhere to intricate or nuanced instructions provided by users.
 * Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
@@ -100,9 +102,9 @@ Coming Soon!
 
 
 ## License
-The model is licensed under the [MIT license](https://huggingface.co/soulhq-ai/phi-2-insurance_qa-sft-lora/blob/main/LICENSE).
+The model is licensed under the [MIT license](https://huggingface.co/deccan-ai/phi-2-insurance_qa-sft-lora/blob/main/LICENSE).
 
 
 ## Citations
 [1] <a id="1" href="https://arxiv.org/abs/2106.09685">Hu, Edward J., et al. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021).</a></br>
-[2] <a id="2" href="https://ieeexplore.ieee.org/abstract/document/7404872/">Feng, Minwei, et al. "Applying deep learning to answer selection: A study and an open task." 2015 IEEE workshop on automatic speech recognition and understanding (ASRU). IEEE, 2015.</a>
+[2] <a id="2" href="https://ieeexplore.ieee.org/abstract/document/7404872/">Feng, Minwei, et al. "Applying deep learning to answer selection: A study and an open task." 2015 IEEE workshop on automatic speech recognition and understanding (ASRU). IEEE, 2015.</a>