README.md

@@ -1,23 +1,21 @@
 ---
 language: multilingual
 widget:
-- text: "🤗"
-- text: "T'estimo! ❤️"
+- text: "T'estimo!"
 - text: "I love you!"
-- text: "I hate you 🤮"
+- text: "I hate you"
 - text: "Mahal kita!"
 - text: "사랑해!"
 - text: "난 너가 싫어"
-- text: "😍😍😍"
 ---
 
 
 # twitter-XLM-roBERTa-base for Sentiment Analysis
 
-This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
+This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis in 
 
-- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://arxiv.org/abs/2104.12250). 
-- Git Repo: [XLM-T official repository](https://github.com/cardiffnlp/xlm-t).
+- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://...). 
+- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/xlm-t).
 
 ## Example Pipeline
 ```python
@@ -26,8 +24,9 @@ model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
 sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
 sentiment_task("T'estimo!")
 ```
+Output: 
 ```
-[{'label': 'Positive', 'score': 0.6600581407546997}]
+[{'label': 'LABEL_2', 'score': 0.6600581407546997}]
 ```
 
 ## Full classification example
@@ -35,7 +34,7 @@ sentiment_task("T'estimo!")
 ```python
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
+from transformers import AutoTokenizer
 import numpy as np
 from scipy.special import softmax
 

config.json

@@ -11,16 +11,16 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "negative",
-    "1": "neutral",
-    "2": "positive"
+    "0": "Negative",
+    "1": "Neutral",
+    "2": "Positive"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "negative": 0,
-    "neutral": 1,
-    "positive": 2
+    "Negative": 0,
+    "Neutral": 1,
+    "Positive": 2
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,