SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, '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': True, '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(
    "<model_name>",
    model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": torch.bfloat16}
)

# Run inference
documents = [
    'Your Upcoming Stay at Park Tower Knightsbridge\n\n\r\n[cid:image001.png@01DA125C.D84FF1A0]\r\n\r\n\r\nDear Abdulla Alhassani,\r\n\r\n\r\n\r\nThank you for choosing The Park Tower Knightsbridge, A Luxury Collection Hotel for your upcoming visit! We are looking forward to welcoming you to the hotel and\r\n\r\nwould like to prepare for your arrival with any special requests you may have. A few details from you, can help us best prepare and make your stay as memorable as possible.\r\n\r\nPlease share with us your estimated arrival time and let us know if we may assist you in booking a private car or taxi service to get to the hotel.\r\n\r\nEmail us here to book your car now.\r\n\r\n\r\n\r\nYOUR RESERVATION\r\n\r\nARRIVAL: 11/11/2023\r\n\r\nDEPARTURE: 11/22/2023\r\n\r\nCONFIRMATION NUMBER: 95476045\r\n\r\n\r\n\r\n*IF TRAVELLING WITH CHILDREN PLEASE CONFIRM THEIR AGES\r\n\r\n\r\n\r\n\r\n\r\n[cid:image002.png@01DA125C.D84FF1A0][cid:image003.png@01DA125C.D84FF1A0][cid:image004.png@01DA125C.D84FF1A0]\r\n\r\n\r\n[cid:image005.png@01DA125C.D84FF1A0]\r\n[cid:image006.png@01DA125C.D84FF1A0][cid:image007.png@01DA125C.D84FF1A0]\r\n[cid:image008.png@01DA125C.D84FF1A0]\n',
]

categories = [
    "Email category: 'Hotel -- Additional request of arrival time'. Email category description: 'A request from the hotel asking for the client to provide the exact or approximate check-in/arrival time as this is requested by the hotel due to different reasons. For example, the hotel does not have 24 hour reception and for this reason is asking for the arrival time. Information about the check-in helps the hotel better prepare for the guest's arrival and plan the schedule of the hotel staff.'",
    "Email category: 'Hotel -- Content request '. Email category description: 'This is an email from a hotelier who has seen photos of the hotel where they work and requests that certain photos be removed, changed, or new ones added. It could also be a request to modify the description of a particular facility or service at the hotel, such as information about type of meals, the deposit amount, or parking facilities. These are important letters that help us keep the hotel photo gallery on the website up to date, ensuring that guests can be confident in what they are booking when looking at the photos. We send such letters to the content department.'",
]

document_embeddings = model.encode(sentences)
category_embeddings = model.encode(sentences)

print(document_embeddings.shape)
# [1, 4096]

print(category_embeddings.shape)
# [2, 4096]

# Get the similarity scores for the embeddings
similarities = model.similarity(document_embeddings, category_embeddings)

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 6
per_device_eval_batch_size: 6
learning_rate: 2e-06
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 6
per_device_eval_batch_size: 6
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 2e-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
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: 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: 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
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.0252	50	0.5621	-
0.0504	100	0.6789	-
0.0755	150	0.7126	-
0.1007	200	0.6461	0.1758
0.1259	250	0.3928	-
0.1511	300	0.3786	-
0.1762	350	0.4105	-
0.2014	400	0.3354	0.1420
0.2266	450	0.327	-
0.2518	500	0.2494	-
0.2769	550	0.1773	-
0.3021	600	0.1215	0.1241
0.3273	650	0.2426	-
0.3525	700	0.2279	-
0.3776	750	0.2151	-
0.4028	800	0.2676	0.1216
0.4280	850	0.2645	-
0.4532	900	0.2491	-
0.4783	950	0.2945	-
0.5035	1000	0.1859	0.1206
0.5287	1050	0.2401	-
0.5539	1100	0.2154	-
0.5791	1150	0.1731	-
0.6042	1200	0.1942	0.1196
0.6294	1250	0.2643	-
0.6546	1300	0.1806	-
0.6798	1350	0.1609	-
0.7049	1400	0.1008	0.1187

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.2.0+cu121
Accelerate: 0.33.0
Datasets: 2.20.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Pasmikh/crm-mail-embedder

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CachedMultipleNegativesRankingLoss

Pasmikh/
crm-mail-embedder