Update README.md

README.md

@@ -8,6 +8,10 @@ metrics:
 model-index:
 - name: hf-bert-finetuning
   results: []
+datasets:
+- zeroshot/twitter-financial-news-sentiment
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,25 +19,27 @@ should probably proofread and complete it, then remove this comment. -->
 
 # hf-bert-finetuning
 
-This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 2.2672
 - Accuracy: 0.805
 
 ## Model description
 
-More information needed
+The base model is [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It was fine-tuned to perform ternary classification (bullish/neutral/bearish) on financial tweets.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to be used for demonstrating how to fine-tune a BERT model using the HuggingFace API. The outputs from the model are not meant to be used in a real production use-case (e.g. to classify whether a tweet is bearish or bullish).
 
 ## Training and evaluation data
 
-More information needed
+The training and evaluation dataset were taken from the [twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset on HuggingFace.
 
 ## Training procedure
 
+100 training and evaluation examples were randomly sampled from the dataset. This was used to train the BERT model for 100 epochs.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -81,4 +87,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.1
 - Pytorch 2.3.0
 - Datasets 2.19.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1