Correct pipeline tag and add Github link

This PR corrects the `pipeline_tag` to `text-generation` which is more appropriate for this model. It also adds a link to the Github repository for easier access to the code.

README.md

@@ -1,11 +1,11 @@
 ---
-library_name: transformers
-license: apache-2.0
-language:
-- en
 base_model:
 - answerdotai/ModernBERT-large
-pipeline_tag: text-classification
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 author: Shreyan C (@thethinkmachine)
 ---
 
@@ -88,9 +88,7 @@ print("Scaled Complexity Score:", get_scaled_complexity_score(query))
 
 ### Training Data
 
-We use [BhabhaAI/DEITA-Complexity](https://huggingface.co/datasets/BhabhaAI/DEITA-Complexity) 'deita'set for training the model. The dataset contains 66.5K diverse English instructions along with their complexity scores computed using the DEITA-Evol-Complexity scoring scheme which uses an LLM-judge to rank a sextuple containing 1 seed + 5 progressively complexified (*evolved*) instructions based on their complexity & difficulty. The scheme assigns scores within [1, 6] range, with 1 being the least complex and 6 being the most complex.
-
-However, the training dataset used was observed to have instruction-score pairs across a diversity of scores within the range [0,9]. We suspect that this range includes scoring errors, as anomalous scores (0, 7, 8, 9) account for less than 1% of the total instructions.
+We use [BhabhaAI/DEITA-Complexity](https://huggingface.co/datasets/BhabhaAI/DEITA-Complexity) 'deita'set for training the model. The dataset contains 66.5K diverse English instructions along with their complexity scores computed using the DEITA-Evol-Complexity scoring scheme which uses an LLM-judge to rank a sextuple containing 1 seed + 5 progressively complexified (*evolved*) instructions based on their complexity & difficulty. The scheme assigns scores within [1, 6] range, with 1 being the least complex and 6 being the most complex. However, the training dataset used was observed to have instruction-score pairs across a diversity of scores within the range [0,9]. We suspect that this range includes scoring errors, as anomalous scores (0, 7, 8, 9) account for less than 1% of the total instructions.
 
 The distribution of scores within the dataset is as follows:
 | Score | Frequency | Relative Freq. |
@@ -142,7 +140,7 @@ You are advised to use the model keeping these factors in mind.
 
 ### CO2 Emissions
 
-Experiments were conducted using Google Cloud Platform in region asia-south1, which has a carbon efficiency of 0.92 kgCO2eq/kWh. A cumulative of 13.24 hours of computation was performed on hardware of type L4 (TDP of 72W).
+Experiments were conducted using Google Cloud Platform in region asia-south1, which has a carbon efficiency of 0.92 kgCO2eq/kWh. A cumulative of 13.24 hours of computation was performed on hardware of type L4 (TDP of 72W).\
 
 Total emissions are estimated to be 0.87 kgCO2eq of which 100% was directly offset by the cloud provider.
 
@@ -164,4 +162,5 @@ For any queries, suggestions or feedback, please contact Shreyan C at *shreyan(a
 - [[2312.15685] What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning](https://arxiv.org/abs/2312.15685)
 - [[2404.02948] PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models](https://arxiv.org/abs/2404.02948)
 - [DEITA-Complexity](https://huggingface.co/datasets/BhabhaAI/DEITA-Complexity)
-- [ModernBERT-Large](https://huggingface.co/answerdotai/ModernBERT-large)
+- [ModernBERT-Large](https://huggingface.co/answerdotai/ModernBERT-large)
+- [Github](https://github.com/thethinkmachine/Maxwell-Task-Complexity-Scorer)