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Model: flax-sentence-embeddings/all_datasets_v3_roberta-large
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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
---
# Model description
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 [`roberta-large`](https://huggingface.co/roberta-large) 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 developped 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 developped 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 Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures
the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence
similarity tasks.
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_roberta-large')
text = "Replace me by any text you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [`roberta-large`](https://huggingface.co/roberta-large). 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 ou model on a TPU v3-8. We train the model during 540k 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.
### 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 |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [COCO 2020](COCO 2020) | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| 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 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [SPECTER](https://github.com/allenai/specter) | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| SearchQA | - | 582,261 |
| [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 |
| [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 |
| [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 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| total | | 1,097,953,922 |

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{
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"""
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 32 --model roberta-large train_data_configs/all_datasets_v3.json output/all_datasets_v3_roberta-large

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