Update README.md

README.md

@@ -6,41 +6,63 @@ model-index:
   results: []
 ---
 
-<!-- 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. -->
+---
+tags:
+- generated_from_keras_callback
+model-index:
+- name: XLM-T-Sent-Politics
+  results: []
+---
+
+# XLM-T-Sent-Politics
+
+This is an "extension" of the `twitter-roberta-base-sentiment-latest` model ([model](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest), further finetuned with original Twitter data posted in English about the 10th anniversary of the 2010 Haiti Earthquake.
 
-# twitter-roberta-base-sentiment-earthquake
+- Reference Paper: [Sentiment analysis (SA) (supervised and unsupervised classification) of original Twitter data posted in English about the 10th anniversary of the 2010 Haiti Earthquake](https://data.ncl.ac.uk/articles/dataset/Sentiment_analysis_SA_supervised_and_unsupervised_classification_of_original_Twitter_data_posted_in_English_about_the_10th_anniversary_of_the_2010_Haiti_Earthquake/19688040/1). 
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
 
+## Full classification example
 
-## Model description
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
 
-More information needed
+class_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
 
-## Intended uses & limitations
+MODEL = "antypasd/twitter-roberta-base-sentiment-earthquake"
 
-More information needed
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
-## Training and evaluation data
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+model.save_pretrained(MODEL)
 
-More information needed
+text = "$202 million of $1.14 billion in United States (US) ​recovery aid went to a new 'industrial park' in Caracol, an area unaffected by the Haiti earthquake. The plan was to invite foreign garment companies to take advantage of extremely low-wage labor"
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+prediction = np.argmax(scores)
 
-## Training procedure
 
-### Training hyperparameters
+# # TF
+# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+# model.save_pretrained(MODEL)
 
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
+# encoded_input = tokenizer(text, return_tensors='tf')
+# output = model(encoded_input)
+# scores = output[0][0].numpy()
+# prediction = np.argmax(scores)
 
-### Training results
+# Print label
+print(text, class_mapping[prediction])
 
+```
 
+Output: 
 
-### Framework versions
+```
+Negative
+```
 
-- Transformers 4.25.1
-- TensorFlow 2.11.0
-- Tokenizers 0.13.2