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Adding tweeteval classifier

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.ipynb_checkpoints/README-checkpoint.md ADDED
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+ # Twitter-roBERTa-base
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+
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+ This is a roBERTa-base model trained on ~58M tweets and finetuned for the Sentiment Analysis task at Semeval 2018.
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+ For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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+ To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
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+
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+ ## Example of classification
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+
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+ ```python
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+ from transformers import AutoModelForSequenceClassification
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+ from transformers import TFAutoModelForSequenceClassification
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+ from transformers import AutoTokenizer
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+ import numpy as np
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+ from scipy.special import softmax
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+ import csv
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+ import urllib.request
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+
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+ # Tasks:
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+ # emoji, emotion, hate, irony, offensive, sentiment
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+ # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
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+
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+ task='sentiment'
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+ MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL)
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+
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+ # download label mapping
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+ labels=[]
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+ mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
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+ with urllib.request.urlopen(mapping_link) as f:
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+ html = f.read().decode('utf-8').split("\n")
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+ spamreader = csv.reader(html[:-1], delimiter='\t')
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+ labels = [row[1] for row in spamreader]
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+
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+ # PT
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+ model = AutoModelForSequenceClassification.from_pretrained(MODEL)
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+ model.save_pretrained(MODEL)
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+
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+ text = "Good night 😊"
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ scores = output[0][0].detach().numpy()
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+ scores = softmax(scores)
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+
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+ # # TF
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+ # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
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+ # model.save_pretrained(MODEL)
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+
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+ # text = "Good night 😊"
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+ # encoded_input = tokenizer(text, return_tensors='tf')
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+ # output = model(encoded_input)
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+ # scores = output[0][0].numpy()
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+ # scores = softmax(scores)
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+
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+ ranking = np.argsort(scores)
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+ ranking = ranking[::-1]
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+ for i in range(scores.shape[0]):
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+ l = labels[ranking[i]]
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+ s = scores[ranking[i]]
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+ print(f"{i+1}) {l} {np.round(float(s), 4)}")
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+
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+ ```
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+
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+ Output:
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+
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+ ```
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+ 1) positive 0.8466
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+ 2) neutral 0.1458
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+ 3) negative 0.0076
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+ ```
README.md ADDED
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+ # Twitter-roBERTa-base
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+
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+ This is a roBERTa-base model trained on ~58M tweets and finetuned for the Sentiment Analysis task at Semeval 2018.
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+ For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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+ To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
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+
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+ ## Example of classification
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+
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+ ```python
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+ from transformers import AutoModelForSequenceClassification
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+ from transformers import TFAutoModelForSequenceClassification
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+ from transformers import AutoTokenizer
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+ import numpy as np
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+ from scipy.special import softmax
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+ import csv
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+ import urllib.request
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+
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+ # Tasks:
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+ # emoji, emotion, hate, irony, offensive, sentiment
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+ # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
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+
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+ task='sentiment'
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+ MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL)
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+
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+ # download label mapping
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+ labels=[]
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+ mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
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+ with urllib.request.urlopen(mapping_link) as f:
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+ html = f.read().decode('utf-8').split("\n")
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+ spamreader = csv.reader(html[:-1], delimiter='\t')
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+ labels = [row[1] for row in spamreader]
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+
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+ # PT
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+ model = AutoModelForSequenceClassification.from_pretrained(MODEL)
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+ model.save_pretrained(MODEL)
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+
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+ text = "Good night 😊"
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ scores = output[0][0].detach().numpy()
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+ scores = softmax(scores)
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+
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+ # # TF
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+ # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
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+ # model.save_pretrained(MODEL)
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+
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+ # text = "Good night 😊"
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+ # encoded_input = tokenizer(text, return_tensors='tf')
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+ # output = model(encoded_input)
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+ # scores = output[0][0].numpy()
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+ # scores = softmax(scores)
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+
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+ ranking = np.argsort(scores)
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+ ranking = ranking[::-1]
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+ for i in range(scores.shape[0]):
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+ l = labels[ranking[i]]
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+ s = scores[ranking[i]]
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+ print(f"{i+1}) {l} {np.round(float(s), 4)}")
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+
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+ ```
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+
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+ Output:
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+
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+ ```
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+ 1) positive 0.8466
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+ 2) neutral 0.1458
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+ 3) negative 0.0076
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "tweeteval_new/roberta-base-rt-sentiment/",
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+ "architectures": [
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+ "RobertaForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_2": 2
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "type_vocab_size": 1,
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+ "vocab_size": 50265
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+ }
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