Update README.md

README.md

@@ -4,34 +4,64 @@ base_model: bert-base-cased
 tags:
 - generated_from_keras_callback
 model-index:
-- name: hausaBERTaa
+- name: hausaBERTa
   results: []
+datasets:
+- mangaphd/hausaBERTdatatrain
+language:
+- ha
+- af
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 probably proofread and complete it, then remove this comment. -->
 
-# hausaBERTaa
+# hausaBERTa
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) trained on mangaphd/hausaBERTdatatrain dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.2333
-- Train Accuracy: 0.9063
+- Train Loss: 0.1186
+- Train Accuracy: 0.9549
 - Epoch: 2
 
 ## Model description
 
-More information needed
+HausaBERTa is a pretrained lexicon low resources language model. The model was trained on Hausa Language (Hausa is a Chadic language spoken by the Hausa people in the northern half of Nigeria, Niger, Ghana, Cameroon, Benin and Togo, and the southern half of Niger, Chad and Sudan, with significant minorities in Ivory Coast. It is the most widely spoken language in West Africa, and one of the most widely spoken languages in Africa as a whole).
+The model has been shown to obtain competitive downstream performances on text classification on trained language
 
 ## Intended uses & limitations
 
-More information needed
+You can use this model with Transformers for sentiment analysis task in Hausa Language.
 
-## Training and evaluation data
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-More information needed
+pipe = pipeline("text-classification", model="mangaphd/hausaBERTmodel")
 
-## Training procedure
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("mangaphd/HausaBERTa")
+model = AutoModelForSequenceClassification.from_pretrained("mangaphd/HausaBERTa")
+
+# Supplementary function
+Add the following codes for ease of interpretation
+
+import pandas as pd
+def sentiment_analysis(text):
+  rs = pipe(text)
+  df = pd.DataFrame(rs)
+  senti=df['label'][0]
+  score=df['score'][0]
+  if senti == 'LABEL_0' and score > 0.5:
+    lb='NEGATIVE'
+  elif senti == 'LABEL_1' and score > 0.5:
+    lb='POSITIVE'
+  else:
+    lb='NEUTRAL'
+  return lb
+
+call sentiment_analysis('Your text here') while using the model
 
 ### Training hyperparameters
 
@@ -43,14 +73,14 @@ The following hyperparameters were used during training:
 
 | Train Loss | Train Accuracy | Epoch |
 |:----------:|:--------------:|:-----:|
-| 0.4838     | 0.7577         | 0     |
-| 0.3030     | 0.8682         | 1     |
-| 0.2333     | 0.9063         | 2     |
+| 0.2108     | 0.9168         | 0     |
+| 0.1593     | 0.9385         | 1     |
+| 0.1186     | 0.9549         | 2     |
 
 
 ### Framework versions
 
-- Transformers 4.34.0
+- Transformers 4.33.2
 - TensorFlow 2.13.0
 - Datasets 2.14.5
-- Tokenizers 0.14.0
+- Tokenizers 0.13.3