Update README.md

README.md

@@ -4,25 +4,37 @@ language:
 - en
 license: apache-2.0
 base_model: google/flan-t5-xl
-model-index:
-- name: flan-t5-xl-summary-map-reduce-1024
-  results: []
 datasets:
 - pszemraj/summary-map-reduce-v1
 pipeline_tag: text2text-generation
+tags:
+- map-reduce
+- summarization
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # flan-t5-xl-summary-map-reduce-1024
 
-This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the pszemraj/summary-map-reduce-v1 dataset.
+A larger t2t model trained to complete the "reduce" step (_consolidation step_) of map-reduce summarization. 
+
+## About 
+
+Refer to [this wiki page](https://github.com/pszemraj/textsum/wiki/consolidating-summaries) page or the [smaller BART model card](https://hf.co/pszemraj/bart-large-summary-map-reduce) for explanations and usage examples. Comparatively, this model seems to
+
+- produce more eloquent final reduced summaries
+- more "gullible"/sensitive to noise in the input summaries
+  - i.e. a hallucinated one-off term/name/entity is likely to be mentioned/appear in the reduced summary 
+- agnostic to whitespace in input (_by definition, since the t5 tokenizer normalizes whitespace_)
+
+Therefore, it's recommended to compare sample outputs of this model and [the BART version](https://hf.co/pszemraj/bart-large-summary-map-reduce) on your data to see which is better for your use case.
+
+## Details 
+
+This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the pszemraj/summary-map-reduce-v1 dataset at 1024 context length in/out.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.6039
 - Num Input Tokens Seen: 7138765
 
-
 ## Training procedure
 
 ### Training hyperparameters
@@ -37,4 +49,4 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 2.0
+- num_epochs: 2.0