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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 irony prediction 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='irony'
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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) 😘 0.2637
68
+ 2) ❀️ 0.1952
69
+ 3) πŸ’• 0.1171
70
+ 4) ✨ 0.0927
71
+ 5) 😊 0.0756
72
+ 6) πŸ’œ 0.046
73
+ 7) πŸ’™ 0.0444
74
+ 8) 😍 0.0272
75
+ 9) πŸ˜‰ 0.0228
76
+ 10) 😎 0.0198
77
+ 11) 😜 0.0166
78
+ 12) πŸ˜‚ 0.0132
79
+ 13) 😁 0.0131
80
+ 14) β˜€ 0.0112
81
+ 15) πŸŽ„ 0.009
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+ 16) πŸ’― 0.009
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+ 17) πŸ”₯ 0.008
84
+ 18) πŸ“· 0.0057
85
+ 19) πŸ‡ΊπŸ‡Έ 0.005
86
+ 20) πŸ“Έ 0.0048
87
+ ```
README.md ADDED
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1
+ # Twitter-roBERTa-base
2
+
3
+ This is a roBERTa-base model trained on ~58M tweets and finetuned for the irony prediction task at Semeval 2018.
4
+ For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
5
+ To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
6
+
7
+ ## Example of classification
8
+
9
+ ```python
10
+ from transformers import AutoModelForSequenceClassification
11
+ from transformers import TFAutoModelForSequenceClassification
12
+ from transformers import AutoTokenizer
13
+ import numpy as np
14
+ from scipy.special import softmax
15
+ import csv
16
+ import urllib.request
17
+
18
+ # Tasks:
19
+ # emoji, emotion, hate, irony, offensive, sentiment
20
+ # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
21
+
22
+ task='irony'
23
+ MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
24
+
25
+ tokenizer = AutoTokenizer.from_pretrained(MODEL)
26
+
27
+ # download label mapping
28
+ labels=[]
29
+ mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
30
+ with urllib.request.urlopen(mapping_link) as f:
31
+ html = f.read().decode('utf-8').split("\n")
32
+ spamreader = csv.reader(html[:-1], delimiter='\t')
33
+ labels = [row[1] for row in spamreader]
34
+
35
+ # PT
36
+ model = AutoModelForSequenceClassification.from_pretrained(MODEL)
37
+ model.save_pretrained(MODEL)
38
+
39
+ text = "Good night 😊"
40
+ encoded_input = tokenizer(text, return_tensors='pt')
41
+ output = model(**encoded_input)
42
+ scores = output[0][0].detach().numpy()
43
+ scores = softmax(scores)
44
+
45
+ # # TF
46
+ # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
47
+ # model.save_pretrained(MODEL)
48
+
49
+ # text = "Good night 😊"
50
+ # encoded_input = tokenizer(text, return_tensors='tf')
51
+ # output = model(encoded_input)
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+ # scores = output[0][0].numpy()
53
+ # scores = softmax(scores)
54
+
55
+ ranking = np.argsort(scores)
56
+ ranking = ranking[::-1]
57
+ for i in range(scores.shape[0]):
58
+ l = labels[ranking[i]]
59
+ s = scores[ranking[i]]
60
+ print(f"{i+1}) {l} {np.round(float(s), 4)}")
61
+
62
+ ```
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+
64
+ Output:
65
+
66
+ ```
67
+ 1) 😘 0.2637
68
+ 2) ❀️ 0.1952
69
+ 3) πŸ’• 0.1171
70
+ 4) ✨ 0.0927
71
+ 5) 😊 0.0756
72
+ 6) πŸ’œ 0.046
73
+ 7) πŸ’™ 0.0444
74
+ 8) 😍 0.0272
75
+ 9) πŸ˜‰ 0.0228
76
+ 10) 😎 0.0198
77
+ 11) 😜 0.0166
78
+ 12) πŸ˜‚ 0.0132
79
+ 13) 😁 0.0131
80
+ 14) β˜€ 0.0112
81
+ 15) πŸŽ„ 0.009
82
+ 16) πŸ’― 0.009
83
+ 17) πŸ”₯ 0.008
84
+ 18) πŸ“· 0.0057
85
+ 19) πŸ‡ΊπŸ‡Έ 0.005
86
+ 20) πŸ“Έ 0.0048
87
+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "tweeteval_new/roberta-base-rt-irony/",
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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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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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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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