Update README.md

Added interpretation

README.md

@@ -1,3 +1,9 @@
+---
+datasets:
+- DIBT/10k_prompts_ranked
+language:
+- en
+---
 Model Card: Uplimit Project 1 part 1
 Model Description:
 This is a model to test run publishing models. It has no real model assessment value.
@@ -17,5 +23,23 @@ hf (pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=float), gen_kwar
 |hellaswag|      1|none  |     0|acc     |↑  |0.2872|±  |0.0045|
 |         |       |none  |     0|acc_norm|↑  |0.3082|±  |0.0046|
 
-How to Use
-To use this model, simply download the checkpoint and load it into your preferred deep learning framework.
+Interpretation (curtesy for Prepelexit.ai):
+
+Accuracy Metrics:
+Standard accuracy: 0.2872 (28.72%)
+Normalized accuracy: 0.3082 (30.82%)
+
+Context:
+The HellaSwag task is a challenging commonsense reasoning benchmark that tests a model's ability to complete sentences or scenarios in a sensible way.
+The task is considered difficult even for larger language models.
+
+Interpretation
+Baseline Performance: The model achieves an accuracy of 28.72% on the standard HellaSwag task, which is significantly above random guessing (25% for a 4-way multiple choice task)1.
+
+Normalized Performance: The normalized accuracy of 30.82% is slightly higher than the standard accuracy, suggesting that the model performs marginally better when accounting for potential biases in the task1.
+
+Model Size Consideration: Given that Pythia 160M is a relatively small language model (160 million parameters), these results are not unexpected2.
+
+Comparative Analysis: While not directly comparable without benchmarks from other models, this performance is likely lower than what larger models (e.g., GPT-3, PaLM) would achieve on the same task2.
+
+Learning Progress: As this is an intermediate checkpoint (step 100000), it's possible that the model's performance could improve with further training