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d1514c3162bbe87b343f565fadc62e6c06f04f03..0000000000000000000000000000000000000000 --- a/embedding_model/1_Pooling/config.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "word_embedding_dimension": 384, - "pooling_mode_cls_token": false, - "pooling_mode_mean_tokens": true, - "pooling_mode_max_tokens": false, - "pooling_mode_mean_sqrt_len_tokens": false -} \ No newline at end of file diff --git a/embedding_model/README.md b/embedding_model/README.md deleted file mode 100644 index 58d4a9a45664eb9e12de9549c548c09b6134c17f..0000000000000000000000000000000000000000 --- a/embedding_model/README.md +++ /dev/null @@ -1,173 +0,0 @@ ---- -language: en -license: apache-2.0 -library_name: sentence-transformers -tags: -- sentence-transformers -- feature-extraction -- sentence-similarity -- transformers -datasets: -- s2orc -- flax-sentence-embeddings/stackexchange_xml -- ms_marco -- gooaq -- yahoo_answers_topics -- code_search_net -- search_qa -- eli5 -- snli -- multi_nli -- wikihow -- natural_questions -- trivia_qa -- embedding-data/sentence-compression -- embedding-data/flickr30k-captions -- embedding-data/altlex -- embedding-data/simple-wiki -- embedding-data/QQP -- embedding-data/SPECTER -- embedding-data/PAQ_pairs -- embedding-data/WikiAnswers -pipeline_tag: sentence-similarity ---- - - -# all-MiniLM-L6-v2 -This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. - -## Usage (Sentence-Transformers) -Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: - -``` -pip install -U sentence-transformers -``` - -Then you can use the model like this: -```python -from sentence_transformers import SentenceTransformer -sentences = ["This is an example sentence", "Each sentence is converted"] - -model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') -embeddings = model.encode(sentences) -print(embeddings) -``` - -## Usage (HuggingFace Transformers) -Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. - -```python -from transformers import AutoTokenizer, AutoModel -import torch -import torch.nn.functional as F - -#Mean Pooling - Take attention mask into account for correct averaging -def mean_pooling(model_output, attention_mask): - token_embeddings = model_output[0] #First element of model_output contains all token embeddings - input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - - -# Sentences we want sentence embeddings for -sentences = ['This is an example sentence', 'Each sentence is converted'] - -# Load model from HuggingFace Hub -tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') -model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') - -# Tokenize sentences -encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') - -# Compute token embeddings -with torch.no_grad(): - model_output = model(**encoded_input) - -# Perform pooling -sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) - -# Normalize embeddings -sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) - -print("Sentence embeddings:") -print(sentence_embeddings) -``` - ------- - -## Background - -The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised -contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a -1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. - -We developed this model during the -[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), -organized by Hugging Face. We developed this model as part of the project: -[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. - -## Intended uses - -Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures -the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. - -By default, input text longer than 256 word pieces is truncated. - - -## Training procedure - -### Pre-training - -We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. - -### Fine-tuning - -We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. -We then apply the cross entropy loss by comparing with true pairs. - -#### Hyper parameters - -We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). -We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with -a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. - -#### Training data - -We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. -We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. - - -| Dataset | Paper | Number of training tuples | -|--------------------------------------------------------|:----------------------------------------:|:--------------------------:| -| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | -| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | -| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | -| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | -| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | -| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | -| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | -| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | -| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | -| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | -| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| -| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | -| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | -| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | -| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | -| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | -| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | -| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | -| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | -| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | -| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | -| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | -| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | -| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | -| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | -| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | -| [TriviaQA](https://huggingface.co/datasets/trivia_qa) 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\ No newline at end of file diff --git a/embedding_model/tokenizer_config.json b/embedding_model/tokenizer_config.json deleted file mode 100644 index c79f2b6a0cea6f4b564fed1938984bace9d30ff0..0000000000000000000000000000000000000000 --- a/embedding_model/tokenizer_config.json +++ /dev/null @@ -1 +0,0 @@ -{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "nreimers/MiniLM-L6-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer", "model_max_length": 512} \ No newline at end of file diff --git a/embedding_model/train_script.py b/embedding_model/train_script.py deleted file mode 100644 index 82b47b6277499b8f17d139d0c651a6f961c06124..0000000000000000000000000000000000000000 --- a/embedding_model/train_script.py +++ /dev/null @@ -1,344 +0,0 @@ -""" -Train script for a single file - -Need to set the TPU address first: -export XRT_TPU_CONFIG="localservice;0;localhost:51011" -""" - -import torch.multiprocessing as mp -import threading -import time -import random -import sys -import argparse -import gzip -import json -import logging -import tqdm -import torch -from torch import nn -from torch.utils.data import DataLoader -import torch -import torch_xla -import torch_xla.core -import torch_xla.core.functions -import torch_xla.core.xla_model as xm -import torch_xla.distributed.xla_multiprocessing as xmp -import torch_xla.distributed.parallel_loader as pl -import os -from shutil import copyfile - - -from transformers import ( - AdamW, - AutoModel, - AutoTokenizer, - get_linear_schedule_with_warmup, - set_seed, -) - -class AutoModelForSentenceEmbedding(nn.Module): - def __init__(self, model_name, tokenizer, normalize=True): - super(AutoModelForSentenceEmbedding, self).__init__() - - self.model = AutoModel.from_pretrained(model_name) - self.normalize = normalize - self.tokenizer = tokenizer - - def forward(self, **kwargs): - model_output = self.model(**kwargs) - embeddings = self.mean_pooling(model_output, kwargs['attention_mask']) - if self.normalize: - embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) - - return embeddings - - def mean_pooling(self, model_output, attention_mask): - token_embeddings = model_output[0] # First element of model_output contains all token embeddings - input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - - def save_pretrained(self, output_path): - if xm.is_master_ordinal(): - self.tokenizer.save_pretrained(output_path) - self.model.config.save_pretrained(output_path) - - xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin")) - - - - -def train_function(index, args, queue): - tokenizer = AutoTokenizer.from_pretrained(args.model) - model = AutoModelForSentenceEmbedding(args.model, tokenizer) - - - ### Train Loop - device = xm.xla_device() - model = model.to(device) - - # Instantiate optimizer - optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True) - - lr_scheduler = get_linear_schedule_with_warmup( - optimizer=optimizer, - num_warmup_steps=500, - num_training_steps=args.steps, - ) - - # Now we train the model - cross_entropy_loss = nn.CrossEntropyLoss() - max_grad_norm = 1 - - model.train() - - for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()): - #### Get the batch data - batch = queue.get() - #print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch]))) - - - if len(batch[0]) == 2: #(anchor, positive) - text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length") - text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length") - - ### Compute embeddings - embeddings_a = model(**text1.to(device)) - embeddings_b = model(**text2.to(device)) - - ### Gather all embedings - embeddings_a = torch_xla.core.functions.all_gather(embeddings_a) - embeddings_b = torch_xla.core.functions.all_gather(embeddings_b) - - ### Compute similarity scores 512 x 512 - scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale - - ### Compute cross-entropy loss - labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i] - - ## Symmetric loss as in CLIP - loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2 - - else: #(anchor, positive, negative) - text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length") - text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length") - text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length") - - embeddings_a = model(**text1.to(device)) - embeddings_b1 = model(**text2.to(device)) - embeddings_b2 = model(**text3.to(device)) - - embeddings_a = torch_xla.core.functions.all_gather(embeddings_a) - embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1) - embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2) - - embeddings_b = torch.cat([embeddings_b1, embeddings_b2]) - - ### Compute similarity scores 512 x 1024 - scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale - - ### Compute cross-entropy loss - labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i] - - ## One-way loss - loss = cross_entropy_loss(scores, labels) - - - # Backward pass - optimizer.zero_grad() - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) - - xm.optimizer_step(optimizer, barrier=True) - lr_scheduler.step() - - - #Save model - if (global_step+1) % args.save_steps == 0: - output_path = os.path.join(args.output, str(global_step+1)) - xm.master_print("save model: "+output_path) - model.save_pretrained(output_path) - - - output_path = os.path.join(args.output, "final") - xm.master_print("save model final: "+ output_path) - model.save_pretrained(output_path) - - -def produce_data(args, queue, filepaths, dataset_indices): - global_batch_size = args.batch_size*args.nprocs #Global batch size - size_per_dataset = int(global_batch_size / args.datasets_per_batch) #How many datasets per batch - num_same_dataset = int(size_per_dataset / args.batch_size) - print("producer", "global_batch_size", global_batch_size) - print("producer", "size_per_dataset", size_per_dataset) - print("producer", "num_same_dataset", num_same_dataset) - - datasets = [] - for filepath in filepaths: - if "reddit_" in filepath: #Special dataset class for Reddit files - data_obj = RedditDataset(filepath) - else: - data_obj = Dataset(filepath) - datasets.append(iter(data_obj)) - - # Store if dataset is in a 2 col or 3 col format - num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)} - - while True: - texts_in_batch = set() - batch_format = None #2 vs 3 col format for this batch - - #Add data from several sub datasets - for _ in range(args.datasets_per_batch): - valid_dataset = False #Check that datasets have the same 2/3 col format - while not valid_dataset: - data_idx = random.choice(dataset_indices) - if batch_format is None: - batch_format = num_cols[data_idx] - valid_dataset = True - else: #Check that this dataset has the same format - valid_dataset = (batch_format == num_cols[data_idx]) - - #Get data from this dataset - dataset = datasets[data_idx] - for _ in range(num_same_dataset): - for _ in range(args.nprocs): - batch_device = [] #A batch for one device - while len(batch_device) < args.batch_size: - sample = next(dataset) - in_batch = False - for text in sample: - if text in texts_in_batch: - in_batch = True - break - - if not in_batch: - for text in sample: - texts_in_batch.add(text) - batch_device.append(sample) - - queue.put(batch_device) - - -class RedditDataset: - """ - A class that handles the reddit data files - """ - def __init__(self, filepath): - self.filepath = filepath - - def __iter__(self): - while True: - with gzip.open(self.filepath, "rt") as fIn: - for line in fIn: - data = json.loads(line) - - if "response" in data and "context" in data: - yield [data["response"], data["context"]] - -class Dataset: - """ - A class that handles one dataset - """ - def __init__(self, filepath): - self.filepath = filepath - - def __iter__(self): - max_dataset_size = 10*1000*1000 #Cache small datasets in memory - dataset = [] - data_format = None - - while dataset is None or len(dataset) == 0: - with gzip.open(self.filepath, "rt") as fIn: - for line in fIn: - data = json.loads(line) - if isinstance(data, dict): - data = data['texts'] - - if data_format is None: - data_format = len(data) - - #Ensure that all entries are of the same 2/3 col format - assert len(data) == data_format - - if dataset is not None: - dataset.append(data) - if len(dataset) >= max_dataset_size: - dataset = None - - yield data - - # Data loaded. Now stream to the queue - # Shuffle for each epoch - while True: - random.shuffle(dataset) - for data in dataset: - yield data - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased') - parser.add_argument('--steps', type=int, default=2000) - parser.add_argument('--save_steps', type=int, default=10000) - parser.add_argument('--batch_size', type=int, default=64) - parser.add_argument('--max_length', type=int, default=128) - parser.add_argument('--nprocs', type=int, default=8) - parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch") - parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product") - parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files") - parser.add_argument('data_config', help="A data_config.json file") - parser.add_argument('output') - args = parser.parse_args() - - # Ensure global batch size is divisble by data_sample_size - assert (args.batch_size*args.nprocs) % args.datasets_per_batch == 0 - - logging.info("Output: "+args.output) - if os.path.exists(args.output): - print("Output folder already exists.") - input("Continue?") - - # Write train script to output path - os.makedirs(args.output, exist_ok=True) - - data_config_path = os.path.join(args.output, 'data_config.json') - copyfile(args.data_config, data_config_path) - - train_script_path = os.path.join(args.output, 'train_script.py') - copyfile(__file__, train_script_path) - with open(train_script_path, 'a') as fOut: - fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv)) - - - - #Load data config - with open(args.data_config) as fIn: - data_config = json.load(fIn) - - queue = mp.Queue(maxsize=100*args.nprocs) - - filepaths = [] - dataset_indices = [] - for idx, data in enumerate(data_config): - filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name'])) - dataset_indices.extend([idx]*data['weight']) - - # Start producer - p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices)) - p.start() - - # Run training - print("Start processes:", args.nprocs) - xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork') - print("Training done") - print("It might be that not all processes exit automatically. In that case you must manually kill this process.") - print("With 'pkill python' you can kill all remaining python processes") - p.kill() - exit() - - - -# Script was called via: -#python train_many_data_files_v2.py --steps 1000000 --batch_size 128 --model nreimers/MiniLM-L6-H384-uncased train_data_configs/all_datasets_v4.json output/all_datasets_v4_MiniLM-L6-H384-uncased-batch128 \ No newline at end of file diff --git a/embedding_model/vocab.txt b/embedding_model/vocab.txt deleted file mode 100644 index fb140275c155a9c7c5a3b3e0e77a9e839594a938..0000000000000000000000000000000000000000 --- a/embedding_model/vocab.txt +++ /dev/null @@ -1,30522 +0,0 @@ -[PAD] -[unused0] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] -[unused74] -[unused75] -[unused76] -[unused77] -[unused78] -[unused79] -[unused80] -[unused81] -[unused82] -[unused83] -[unused84] -[unused85] -[unused86] -[unused87] -[unused88] -[unused89] -[unused90] -[unused91] -[unused92] -[unused93] -[unused94] -[unused95] -[unused96] -[unused97] -[unused98] -[UNK] -[CLS] -[SEP] -[MASK] -[unused99] -[unused100] -[unused101] -[unused102] -[unused103] -[unused104] -[unused105] -[unused106] -[unused107] 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