readme: ---
  language: 
    - es
  ---
  
  # beto-emoji
  Fine-tunning [BETO](https://github.com/dccuchile/beto) for emoji-prediction.
  
  ## Repository
  Details with training and a use example are shown in [github.com/camilocarvajalreyes/beto-emoji](https://github.com/camilocarvajalreyes/beto-emoji). A deeper analysis of this and other models on the full dataset can be found in [github.com/furrutiav/data-mining-2022](https://github.com/furrutiav/data-mining-2022). We have used this model for a project for [CC5205 Data Mining](https://github.com/dccuchile/CC5205) course.
  
  ## Example
  Inspired by model card from [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji).
  
  ```python
  from transformers import AutoModelForSequenceClassification
  from transformers import TFAutoModelForSequenceClassification
  from transformers import AutoTokenizer
  import numpy as np
  from scipy.special import softmax
  import csv
  import urllib.request
  # 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 = f"ccarvajal/beto-emoji"
  tokenizer = AutoTokenizer.from_pretrained(MODEL)
  # download label mapping
  labels=[]
  mapping_link = f"https://raw.githubusercontent.com/camilocarvajalreyes/beto-emoji/main/es_mapping.txt"
  with urllib.request.urlopen(mapping_link) as f:
      html = f.read().decode('utf-8').split("\n")
      csvreader = csv.reader(html, delimiter='\t')
  labels = [row[1] for row in csvreader if len(row) > 1]
  
  model = AutoModelForSequenceClassification.from_pretrained(MODEL)
  model.save_pretrained(MODEL)
  text =  "que viva españa"
  text = preprocess(text)
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  scores = output[0][0].detach().numpy()
  scores = softmax(scores)
  
  ranking = np.argsort(scores)
  ranking = ranking[::-1]
  for i in range(scores.shape[0]):
      l = labels[ranking[i]]
      s = scores[ranking[i]]
      print(f"{i+1}) {l} {np.round(float(s), 4)}")
  ```
  Output
  
  ```python
  1) 🇪🇸 0.2508
  2) 😍 0.238
  3) 👌 0.2225
  4) 😂 0.0806
  5) ❤ 0.0489
  6) 😁 0.0415
  7) 😜 0.0232
  8) 😎 0.0229
  9) 😊 0.0156
  10) 😉 0.0119
  11) 💜 0.0079
  12) 💕 0.0077
  13) 💪 0.0066
  14) 💘 0.0054
  15) 💙 0.0052
  16) 💞 0.005
  17) 😘 0.0034
  18) 🎶 0.0022
  19) ✨ 0.0007
  ```
  
  ## Results in test set
                   precision    recall  f1-score   support
  
             ❤       0.39      0.43      0.41      2141
             😍       0.29      0.39      0.33      1408
             😂       0.51      0.51      0.51      1499
             💕       0.09      0.05      0.06       352
             😊       0.12      0.23      0.16       514
             😘       0.24      0.23      0.24       397
             💪       0.37      0.43      0.40       307
             😉       0.15      0.17      0.16       453
             👌       0.09      0.16      0.11       180
             🇪🇸       0.46      0.46      0.46       424
             😎       0.12      0.11      0.11       339
             💙       0.36      0.02      0.04       413
             💜       0.00      0.00      0.00       235
             😜       0.04      0.02      0.02       274
             💞       0.00      0.00      0.00        93
             ✨       0.26      0.12      0.17       416
             🎶       0.25      0.24      0.24       212
             💘       0.00      0.00      0.00       134
             😁       0.05      0.03      0.04       209
  
       accuracy                           0.30     10000
      macro_avg       0.20      0.19      0.18     10000
      weighted avg    0.29      0.30      0.29     10000
  
  [Another example](https://github.com/camilocarvajalreyes/beto-emoji/blob/main/attention_visualisation.ipynb) with a visualisation of the attention modules within this model is carried out using [bertviz](https://github.com/jessevig/bertviz).
  
  ## Reproducibility
  The Multilingual Emoji Prediction dataset (Barbieri et al. 2010) consists of tweets in English and Spanish that originally had a single emoji, which is later used as a tag. Test and trial sets can be downloaded [here](https://github.com/fvancesco/Semeval2018-Task2-Emoji-Detection/blob/master/dataset/Semeval2018-Task2-EmojiPrediction.zip?raw=true), but the train set needs to be downloaded using a [twitter crawler](https://github.com/fra82/twitter-crawler/blob/master/semeval2018task2TwitterCrawlerHOWTO.md). The goal is to predict that single emoji that was originally in the tweet using the text in it (out of a fixed set of possible emojis, 20 for English and 19 for Spanish).
  
  Training parameters:
  ```python
  training_args = TrainingArguments(
      output_dir="./results",
      learning_rate=2e-5,
      per_device_train_batch_size=16,
      per_device_eval_batch_size=16,
      num_train_epochs=5,
      weight_decay=0.01
  )
  ```
  