This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. It maps sentences & paragraphs to a 384-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: intfloat/multilingual-e5-small
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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, 'architecture': 'BertModel'})
  (1): Pooling({'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, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Can I automate the Gain image over time to create dynamic volume changes?',
    'Document_title: Harmor \nFile_name: plugins/Harmor.htm\nHeading_hierarchy: [Harmor -> About images and planes]\nAnchor_id: [none]\nThere are independent images that control the Pitch/Frequency and Gain of partials. Together these can create any sound, just as sampler can. In the image window the vertical dimension\n  is frequency (each line of pixels is a single partial), while the horizontal dimension is time.',
    'Document_title: PoiZone V2 \nFile_name: plugins/PoiZone.htm\nHeading_hierarchy: [PoiZone V2 -> Voicing]\nAnchor_id: [none]\n• 2 main oscillators for subtractive synthesis: SAW and PULSE shapes, pulse width adjustable. • 1 NOISE Oscillator. • Variable polyphony (1 to 32 voices).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4301, 0.1394],
#         [0.4301, 1.0000, 0.1051],
#         [0.1394, 0.1051, 1.0000]])

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6911
cosine_accuracy@3	0.878
cosine_accuracy@5	0.9252
cosine_accuracy@10	0.9643
cosine_precision@1	0.6911
cosine_precision@3	0.2927
cosine_precision@5	0.185
cosine_precision@10	0.0964
cosine_recall@1	0.6911
cosine_recall@3	0.878
cosine_recall@5	0.9252
cosine_recall@10	0.9643
cosine_ndcg@10	0.834
cosine_mrr@10	0.7914
cosine_map@100	0.7931

Training Details

Training Dataset

Unnamed Dataset

Size: 29,840 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 9 tokens
mean: 19.39 tokens
max: 34 tokens
min: 29 tokens
mean: 324.18 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
`How do I load a *.SPEECH preset into the Sampler plugin?`	`Document_title: Speech Preset ([object Object].SPEECH)][object Object]Anchor_id: [none][object Object]The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for[object Object] synthesizing, i.e. [object Object] , [object Object] , [object Object] and [object Object] .`
`Can I use Speech presets with the Fruity Granulizer to create vocal effects?`	`Document_title: Speech Preset ([object Object].SPEECH)][object Object]Anchor_id: [none][object Object]The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for[object Object] synthesizing, i.e. [object Object] , [object Object] , [object Object] and [object Object] .`
`What kind of vocals can the Speech synthesizer create from text?`	`Document_title: Speech Preset ([object Object].SPEECH)][object Object]Anchor_id: [none][object Object]The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for[object Object] synthesizing, i.e. [object Object] , [object Object] , [object Object] and [object Object] .`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 2
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
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: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs