Create README.md

README.md

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+---
+language: 
+- es
+
+tags:
+- Emotion Analysis
+
+---
+
+**Note**: This model & model card are based on the [finetuned XLM-T for Sentiment Analysis](cardiffnlp/twitter-xlm-roberta-base-sentiment)
+
+# twitter-XLM-roBERTa-base for Emotion Analysis
+This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for emotion analysis on Spanish language. This model was presented to EmoEvalEs competition, part of [IberLEF 2021 Conference](https://sites.google.com/view/iberlef2021/), where the proposed task was the classification of Spanish tweets between seven different classes: *anger*, *disgust*, *fear*, *joy*, *sadness*, *surprise*, and *other*. We achieved the first position in the competition with a macro-averaged F1 score of 71.70%. 
+- [Our code for EmoEvalEs submission](https://github.com/gsi-upm/emoevales-iberlef2021).
+- [EmoEvalEs Dataset](https://github.com/pendrag/EmoEvalEs)
+## Example Pipeline with a [Tweet from @JaSantaolalla](https://twitter.com/JaSantaolalla/status/1398383243645177860)
+```python
+from transformers import pipeline
+model_path = "daveni/twitter-xlm-roberta-emotion-es"
+tokenizer_path = 'cardiffnlp/twitter-xlm-roberta-base'
+emotion_analysis = pipeline("text-classification", model=model_path, tokenizer=tokenizer_path)
+emotion_analysis("Einstein dijo: Solo hay dos cosas infinitas, el universo y los pinches anuncios de bitcoin en Twitter. Paren ya carajo aaaaaaghhgggghhh me quiero murir")
+```
+```
+[{'label': 'anger', 'score': 0.48307016491889954}]
+```
+## Full classification example
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+model_path = "daveni/twitter-xlm-roberta-emotion-es"
+tokenizer_path = 'cardiffnlp/twitter-xlm-roberta-base'
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_path )
+config = AutoConfig.from_pretrained(model_path )
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(model_path )
+text = "Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal."
+text = preprocess(text)
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+scores = softmax(scores)
+
+# Print labels and scores
+ranking = np.argsort(scores)
+ranking = ranking[::-1]
+for i in range(scores.shape[0]):
+    l = config.id2label[ranking[i]]
+    s = scores[ranking[i]]
+    print(f"{i+1}) {l} {np.round(float(s), 4)}")
+```
+Output: 
+```
+Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal.
+1) joy 0.7887
+2) others 0.1679
+3) surprise 0.0152
+4) sadness 0.0145
+5) anger 0.0077
+6) disgust 0.0033
+7) fear 0.0027
+```
+
+#### Limitations and bias
+
+- The dataset we used for finetuning was unbalanced, where almost half of the records belonged to the *other* class so there might be bias towards this class.
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by [cardiffnlp](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base). We used the [EmoEvalEs Dataset](https://github.com/pendrag/EmoEvalEs) for finetuning.
+
+### BibTeX entry and citation info
+
+```bibtex
+Coming soon
+```