Update README.md

README.md

@@ -13,9 +13,9 @@ license: apache-2.0
 
 This model was trained as part of the analysis and experiments performed in preparation of the release of the [Institutional Books 1.0 dataset](https://huggingface.co/collections/instdin/institutional-books-68366258bfb38364238477cf).
 
-It is a text classifier, that we used to assign 1 of 20 topics, derived from the first level of the [Library of Congress' Classification Outline](https://www.loc.gov/catdir/cpso/lcco/), to individual volumes.
+We used this text classifier to assign 1 of 20 topics, derived from the first level of the [Library of Congress' Classification Outline](https://www.loc.gov/catdir/cpso/lcco/), to individual volumes.
 
-Complete experimental setup and results are available in our [technical report]() (Section 4.5).
+Complete experimental setup and results are available in our [technical report](TBD) (Section 4.5).
 
 ## Base model
 [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased)
@@ -57,8 +57,7 @@ All of the fields listed in this example are optional.
 ## Training data
 - Train split: 80,830 samples
 - Test split: 5,000 samples
-
-An additional set of 1,000 samples was set aside for benchmarking purposes.
+- An additional set of 1,000 samples was set aside for benchmarking purposes
 
 ## Validation Metrics
 | Metric | Value |
@@ -75,7 +74,7 @@ An additional set of 1,000 samples was set aside for benchmarking purposes.
 | recall_weighted | 0.9694 |
 | accuracy | 0.9694 |
 
-**Benchmark accuracy:** 97.2% (920)
+**Post-training benchmark accuracy:** 97.2% (920) 
 
 ## Cite
 ```