Update README.md

README.md

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----
-tags:
-- generated_from_keras_callback
-model-index:
-- name: tf version
-  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. -->
-
-# tf version
-
-This model is a fine-tuned version of [antypasd/tweet-topic-21-single](https://huggingface.co/antypasd/tweet-topic-21-single) on an unknown dataset.
-It achieves the following results on the evaluation set:
-
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.19.2
-- TensorFlow 2.8.2
-- Tokenizers 0.12.1
+# tweet-topic-21-single
+
+This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
+The original roBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
+
+- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
+- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
+
+<b>Labels</b>: 
+- 0 -> arts_&_culture;
+- 1 -> business_&_entrepreneurs;
+- 2 -> pop_culture;
+- 3 -> daily_life;
+- 4 -> sports_&_gaming;
+- 5 -> science_&_technology
+
+
+## Full classification example
+
+```python
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+
+    
+MODEL = f"cardiffnlp/tweet-topic-21-single"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+class_mapping = model.config.id2label
+
+text = "Tesla stock is on the rise!"
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+scores = softmax(scores)
+
+# TF
+#model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+#class_mapping = model.config.id2label
+#text = "Tesla stock is on the rise!"
+#encoded_input = tokenizer(text, return_tensors='tf')
+#output = model(**encoded_input)
+#output = model(**encoded_input)
+#scores = output[0][0]
+#scores = softmax(scores)
+
+
+ranking = np.argsort(scores)
+ranking = ranking[::-1]
+for i in range(scores.shape[0]):
+    l = class_mapping[ranking[i]]
+    s = scores[ranking[i]]
+    print(f"{i+1}) {l} {np.round(float(s), 4)}")
+
+```
+
+Output: 
+
+```
+1) business_&_entrepreneurs 0.8361
+2) science_&_technology 0.0904
+3) pop_culture 0.0288
+4) daily_life 0.0178
+5) arts_&_culture 0.0137
+6) sports_&_gaming 0.0133
+```