Update README.md

README.md

@@ -3,9 +3,17 @@ license: apache-2.0
 base_model: bert-base-uncased
 tags:
 - generated_from_keras_callback
+- really-cool
 model-index:
 - name: bert-base-uncased-finetuned-glue-sst2
   results: []
+datasets:
+- glue
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -13,21 +21,25 @@ probably proofread and complete it, then remove this comment. -->
 
 # bert-base-uncased-finetuned-glue-sst2
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+Use for **sentiment analysis**. Labels: `positive`, `negative`
+
+This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model, fine-tuned on a subset of the [glue sst2 dataset](https://huggingface.co/datasets/glue/viewer/sst2).
+
 It achieves the following results on the evaluation set:
 
+```
+Evaluation Accuracy: 91.74% 
+```
 
 ## Model description
 
-More information needed
+The `bert-base-uncased` model is a pretrained English language model which has learned a bidirectional representation through Masked Language Modeling (MLM).
 
-## Intended uses & limitations
-
-More information needed
+The `bert-base-uncased-finetuned-glue-sst2` adds a 2-class classification head to `bert-base-uncased`. It is then fine-tuned for **sentiment analysis** on the [glue sst2 dataset](https://huggingface.co/datasets/glue/viewer/sst2).
 
 ## Training and evaluation data
 
-More information needed
+This model was only trained on 10000 samples, while the entire glue sst2 training set includes 67349 examples. This was done mainly to decrease training time.
 
 ## Training procedure
 
@@ -39,11 +51,12 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+- Accuracy (training): `94.08%`
+- Accuracy (validation): `91.74%`
 
 ### Framework versions
 
 - Transformers 4.35.2
 - TensorFlow 2.15.0
 - Datasets 2.16.1
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0