Update README.md

README.md

@@ -1,6 +1,6 @@
 # tweet-topic-21-multi
 
-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 11,267 tweets.
+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 multi-label topic classification on a corpus of 11,267 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). 
@@ -20,7 +20,7 @@ The original roBERTa-base model can be found [here](https://huggingface.co/cardi
 ## Full classification example
 
 ```python
-from transformers import AutoModelForSequenceClassification
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer
 import numpy as np
 from scipy.special import expit
@@ -41,6 +41,17 @@ scores = output[0][0].detach().numpy()
 scores = expit(scores)
 predictions = (scores >= 0.5) * 1
 
+
+# TF
+#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+#class_mapping = model.config.id2label
+#text = "It is great to see athletes promoting awareness for climate change."
+#tokens = tokenizer(text, return_tensors='tf')
+#output = tf_model(**tokens)
+#scores = output[0][0]
+#scores = expit(scores)
+#predictions = (scores >= 0.5) * 1
+
 # Map to classes
 for i in range(len(predictions)):
   if predictions[i]: