init

experiments/huggingface_ops.py

@@ -2,8 +2,8 @@ from huggingface_hub import HfApi, ModelFilter
 from pprint import pprint
 
 api = HfApi()
-models = api.list_models(filter=ModelFilter(author='vocabtrimmer'))
-models_filtered = [i.modelId for i in models if 'cardiffnlp/twitter-roberta-base-jun2020' in i.modelId]
+models = api.list_models(filter=ModelFilter(author='tweettemposhift'))
+models_filtered = [i.modelId for i in models if 'twitter-roberta-base-sep2021' in i.modelId]
 pprint(sorted(models_filtered))
 # models = api.list_models(filter=ModelFilter(author='tweettemposhift'))
 # models_filtered = [i.modelId for i in models if 'topic-' in i.modelId]

experiments/main.sh

@@ -71,6 +71,8 @@ python model_finetuning_topic.py -m "${MODEL}" -d "topic_random1_seed2"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random2_seed2"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random3_seed2"
 
+# Rerun eval for NER
+
 download () {
   git clone "https://huggingface.co/tweettemposhift/ner-${1}-${2##*/}"
   mkdir "ckpt/ner-${1}-${2##*/}"

experiments/model_predict_ner.py

@@ -1,110 +1,110 @@
-# #WIP
-# import re
-# import os
-# import torch
-# import json
-# from typing import Dict, List
-# from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoConfig
-# from datasets import load_dataset
-#
-# URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
-# HANDLE_RE = re.compile(r"@\w+")
-#
-#
-# def preprocess_bernice(text):
-#     text = HANDLE_RE.sub("@USER", text)
-#     text = URL_RE.sub("HTTPURL", text)
-#     return text
-#
-#
-# def preprocess_timelm(text):
-#     text = HANDLE_RE.sub("@user", text)
-#     text = URL_RE.sub("http", text)
-#     return text
-#
-#
-# def preprocess(model_name, text):
-#     if model_name == "jhu-clsp/bernice":
-#         return preprocess_bernice(text)
-#     if "twitter-roberta-base" in model_name:
-#         return preprocess_timelm(text)
-#     return text
-#
-#
-# class NER:
-#
-#     def __init__(self, model_name: str, max_length: int, id_to_label: Dict[str, str]):
-#         self.model_name = model_name
-#         self.config = AutoConfig.from_pretrained(self.model_name)
-#         self.model = AutoModelForTokenClassification.from_pretrained(self.model_name, config=self.config)
-#         self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-#         self.max_length = max_length
-#         self.id_to_label = id_to_label
-#         # GPU setup (https://github.com/cardiffnlp/tweetnlp/issues/15)
-#         if torch.cuda.is_available() and torch.cuda.device_count() > 0:
-#             self.device = torch.device('cuda')
-#         elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and torch.backends.mps.is_built():
-#             self.device = torch.device("mps")
-#         else:
-#             self.device = torch.device('cpu')
-#         self.parallel = torch.cuda.device_count() > 1
-#         if self.parallel:
-#             self.model = torch.nn.DataParallel(self.model)
-#         self.model.to(self.device)
-#         self.model.eval()
-#         self.dataset = load_dataset("tweettemposhift/tweet_temporal_shift", "ner_temporal")
-#
-#     def get_prediction(self, export_dir: str, batch_size: int):
-#         os.makedirs(export_dir, exist_ok=True)
-#         for test_split in ["test_1", "test_2", "test_3", "test_4"]:
-#             data = self.dataset[test_split]
-#             predictions = self.predict(data["text"], batch_size)
-#             with open(f"{export_dir}/{test_split}.jsonl", "w") as f:
-#                 f.write("\n".join([json.dumps(i) for i in predictions]))
-#
-#         with open(export_dir, "w") as f:
-#             predictions = self.predict(self.dataset[], batch_size)
-#             for i in :
-#                 f.write(json.dumps(i) + "\n")
-#
-#     def predict(self, text: List[str], batch_size: int):
-#         text = [[preprocess(self.model_name, t) for t in i] for i in text]
-#         indices = list(range(0, len(text), batch_size)) + [len(text) + 1]
-#         inputs = []
-#         preds = []
-#         with torch.no_grad():
-#             for i in range(len(indices) - 1):
-#                 encoded_input = self.tokenizer.batch_encode_plus(
-#                     text[indices[i]: indices[i + 1]],
-#                     max_length=self.max_length,
-#                     return_tensors='pt',
-#                     padding=True,
-#                     truncation=True)
-#                 inputs += encoded_input['input_ids'].cpu().detach().int().tolist()
-#                 output = self.model(**{k: v.to(self.device) for k, v in encoded_input.items()})
-#                 prob = torch.softmax(output['logits'], dim=-1).cpu().detach().float().tolist()
-#                 pred = torch.max(prob, dim=-1)[1].cpu().detach().int().tolist()
-#                 preds += [[self.id_to_label[_p] for _p in p] for p in pred]
-#         return [{"label": p, "input_id": i} for p, i in zip(preds, inputs)]
-#
-#
-# if __name__ == '__main__':
-#     model_list = [
-#         "roberta-base",
-#         "bertweet-base",
-#         "bernice",
-#         "roberta-large",
-#         "bertweet-large",
-#         "twitter-roberta-base-2019-90m",
-#         "twitter-roberta-base-dec2020",
-#         "twitter-roberta-base-2021-124m",
-#         "twitter-roberta-base-2022-154m",
-#         "twitter-roberta-large-2022-154m"
-#     ]
-#     for model_m in model_list:
-#         alias = f"tweettemposhift/ner-ner_temporal-{model_m}"
-#         NER(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)
-#         for random_r in range(4):
-#             for seed_s in range(3):
-#                 alias = f"tweettemposhift/ner-ner_random{random_r}_seed{seed_s}-{model_m}"
-#                 TopicClassification(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)
+import re
+import os
+import torch
+import json
+from typing import Dict, List
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoConfig
+from datasets import load_dataset
+
+
+URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
+HANDLE_RE = re.compile(r"@\w+")
+
+
+def preprocess_bernice(text):
+    text = HANDLE_RE.sub("@USER", text)
+    text = URL_RE.sub("HTTPURL", text)
+    return text
+
+
+def preprocess_timelm(text):
+    text = HANDLE_RE.sub("@user", text)
+    text = URL_RE.sub("http", text)
+    return text
+
+
+def preprocess(model_name, text):
+    if model_name == "jhu-clsp/bernice":
+        return preprocess_bernice(text)
+    if "twitter-roberta-base" in model_name:
+        return preprocess_timelm(text)
+    return text
+
+
+class NER:
+
+    def __init__(self, model_name: str, max_length: int, id_to_label: Dict[str, str]):
+        self.model_name = model_name
+        self.config = AutoConfig.from_pretrained(self.model_name)
+        self.model = AutoModelForTokenClassification.from_pretrained(self.model_name, config=self.config)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.max_length = max_length
+        self.id_to_label = id_to_label
+        # GPU setup (https://github.com/cardiffnlp/tweetnlp/issues/15)
+        if torch.cuda.is_available() and torch.cuda.device_count() > 0:
+            self.device = torch.device('cuda')
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and torch.backends.mps.is_built():
+            self.device = torch.device("mps")
+        else:
+            self.device = torch.device('cpu')
+        self.parallel = torch.cuda.device_count() > 1
+        if self.parallel:
+            self.model = torch.nn.DataParallel(self.model)
+        self.model.to(self.device)
+        self.model.eval()
+        self.dataset = load_dataset("tweettemposhift/tweet_temporal_shift", "ner_temporal")
+
+    def get_prediction(self, export_dir: str, batch_size: int):
+        os.makedirs(export_dir, exist_ok=True)
+        for test_split in ["test_1", "test_2", "test_3", "test_4"]:
+            data = self.dataset[test_split]
+            predictions = self.predict(data["text"], batch_size)
+            with open(f"{export_dir}/{test_split}.jsonl", "w") as f:
+                f.write("\n".join([json.dumps(i) for i in predictions]))
+
+        with open(export_dir, "w") as f:
+            predictions = self.predict(self.dataset[], batch_size)
+            for i in :
+                f.write(json.dumps(i) + "\n")
+
+    def predict(self, text: List[str], batch_size: int):
+        text = [[preprocess(self.model_name, t) for t in i] for i in text]
+        indices = list(range(0, len(text), batch_size)) + [len(text) + 1]
+        inputs = []
+        preds = []
+        with torch.no_grad():
+            for i in range(len(indices) - 1):
+                encoded_input = self.tokenizer.batch_encode_plus(
+                    text[indices[i]: indices[i + 1]],
+                    max_length=self.max_length,
+                    return_tensors='pt',
+                    padding=True,
+                    truncation=True)
+                inputs += encoded_input['input_ids'].cpu().detach().int().tolist()
+                output = self.model(**{k: v.to(self.device) for k, v in encoded_input.items()})
+                prob = torch.softmax(output['logits'], dim=-1).cpu().detach().float().tolist()
+                pred = torch.max(prob, dim=-1)[1].cpu().detach().int().tolist()
+                preds += [[self.id_to_label[_p] for _p in p] for p in pred]
+        return [{"label": p, "input_id": i} for p, i in zip(preds, inputs)]
+
+
+if __name__ == '__main__':
+    model_list = [
+        "roberta-base",
+        "bertweet-base",
+        "bernice",
+        "roberta-large",
+        "bertweet-large",
+        "twitter-roberta-base-2019-90m",
+        "twitter-roberta-base-dec2020",
+        "twitter-roberta-base-2021-124m",
+        "twitter-roberta-base-2022-154m",
+        "twitter-roberta-large-2022-154m"
+    ]
+    for model_m in model_list:
+        alias = f"tweettemposhift/ner-ner_temporal-{model_m}"
+        NER(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)
+        for random_r in range(4):
+            for seed_s in range(3):
+                alias = f"tweettemposhift/ner-ner_random{random_r}_seed{seed_s}-{model_m}"
+                TopicClassification(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)