SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: FacebookAI/xlm-roberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Most Common Apple Varieties',
    'The most popular apple varieties are Cortland, Red Delicious, Golden Delicious, Empire, Fuji, Gala, Ida Red, Macoun, McIntosh, Northern Spy, and Winesap. Olwen Woodier also offers descriptions for an additional 20 varieties of apples in this very useful and informative cookbook. Cortland.',
    'Well, rest easy, because this condensed list of the 18 most popular apple varieties breaks down the information every apple eater should knowâ\x80\x94how to cook them, best recipes, and when they are in season. Red Delicious: A popular eating apple that looks just how we all imagine an apple should.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 502,912 training samples
Columns: sentence_0, sentence_1, sentence_2, and label

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2	label
type	string	string	string	float
details	min: 4 tokens mean: 9.88 tokens max: 59 tokens	min: 19 tokens mean: 88.5 tokens max: 232 tokens	min: 17 tokens mean: 87.87 tokens max: 282 tokens	min: -16.56 mean: 0.96 max: 20.84

Samples:

sentence_0	sentence_1	sentence_2	label
`how long are bank issued checks good for`	`Your mom is correct....most checks are good for anywhere between 180 days up to 1 year. Sorry, but you probably won't be able to cash those checks, although it never hurts to check with your bank on the issue. DH Â· 9 years ago.`	`Non-local personal and business checks. If the check is from a bank in a different federal reserve district than the depositing bank, it can be held for 5 business days under normal circumstances. Exceptions for new customers during the first 30 days. Banks are not required to give next day ability on the first $100 of deposits, and both local and non-local personal and business checks can be held for a maximum of 11 business days.`	`2.6526598930358887`
`11:11 meaning`	`11-11-11 11:11:11 example. 11-11 11:11 example. Numerologists believe that events linked to the time 11:11 appear more often than can be explained by chance or coincidence. This belief is related to the concept of synchronicity. Some authors claim that seeing 11:11 on a clock is an auspicious sign.`	`Sometimes it's difficult to describe what seeing the 11:11 means, because it is a personal experience for everyone. If you feel you are having these experiences for a reason, then it might be that only you will know what these number prompts and wake-up calls mean.`	`-1.3284940719604492`
`did someone from pawn stars die`	`Did someone from pawn stars on history channel die? kgb answers Â» Arts & Entertainment Â» Actors and Actresses Â» Did someone from pawn stars on history channel die? None from the actors & cast of Pawn Stars died. There was a rumor that Leonard Shaffer, a coin expert, died but it is not true. He is alive & well. Tags: pawn stars, lists of actors.`	`Austin Russell, also known as Chumlee, star of History's reality series Pawn Stars, has died from an apparent heart attack, sources confirm to eBuzzd.`	`1.7131614685058594`

Loss: MarginMSELoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 30
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 30
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs