SentenceTransformer based on BAAI/bge-large-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5 on the spectrum-design-docs dataset. It maps sentences & paragraphs to a 1024-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: BAAI/bge-large-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- spectrum-design-docs
Language: en

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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("JianLiao/spectrum-doc-fine-tuned")
# Run inference
sentences = [
    'Represent this sentence for searching relevant passages: How can a designer balance the need for clear text links and the need for emphasized text in a user interface?',
    "Typography\nUsage guidelines\nDon't use underlines for adding emphasis: Underlines are reserved for text links only. They should not be used as a way for adding emphasis to words.\n\n",
    'Meter\nOptions\nPositive variant: The positive variant has a green fill to show the value. This can be used to represent a positive semantic value, such as when there’s a lot of space remaining.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Dataset: sds
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0075
cosine_accuracy@3	0.0156
cosine_accuracy@5	0.0475
cosine_accuracy@10	0.7815
cosine_precision@1	0.0075
cosine_precision@3	0.0052
cosine_precision@5	0.0095
cosine_precision@10	0.0782
cosine_recall@1	0.0075
cosine_recall@3	0.0156
cosine_recall@5	0.0475
cosine_recall@10	0.7815
cosine_ndcg@10	0.2544
cosine_mrr@10	0.1078
cosine_map@100	0.1164

Training Details

Training Dataset

spectrum-design-docs

Dataset: spectrum-design-docs at 23f5565
Size: 14,737 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 20 tokens
mean: 30.87 tokens
max: 47 tokens

min: 18 tokens
mean: 97.17 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 20 tokens mean: 30.87 tokens max: 47 tokens	min: 18 tokens mean: 97.17 tokens max: 512 tokens

Samples:

anchor	positive
`Represent this sentence for searching relevant passages: Are there any specific guidelines or best practices provided by the Spectrum team for integrating Spectrum CSS into a new or existing project?`	`Spectrum CSS: An open source CSS-only implementation of Spectrum, maintained by the Spectrum team. Dependency chain: Spectrum DNA → Spectrum CSS GitHub repository Website #spectrum_css`
`Represent this sentence for searching relevant passages: How does the default setting for progress circles affect their behavior in a UI?`	`Progress circle Options Indeterminate: A progress circle can be either determinate or indeterminate. By default, progress circles are determinate. Use a determinate progress circle when progress can be calculated against a specific goal (e.g., downloading a file of a known size). Use an indeterminate progress circle when progress is happening but the time or effort to completion can’t be determined (e.g., attempting to reconnect to a server).`
`Represent this sentence for searching relevant passages: What tools or methods can designers use to test the effectiveness of wrapped legends in their designs?`	`Legend Behaviors Wrapping: When there isn’t enough space, wrap legends to ensure that dimension values are shown.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 22
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 100
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
prompts: {'anchor': 'Represent this sentence for searching relevant passages: '}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 22
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 100
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
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: True
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_fused
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: {'anchor': 'Represent this sentence for searching relevant passages: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs