SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the json 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: intfloat/multilingual-e5-large
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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: PeftModelForFeatureExtraction 
  (1): Pooling({'word_embedding_dimension': 1024, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: What is the lump sum payment for professional outplacement services that the Executive will receive?',
    'be entitled to receive continuing group medical coverage for himself and his dependents (on a non-taxable basis, including if necessary, payment of any gross-up payments necessary to result in net non-taxable benefits), which coverage is not materially less favorable to the Executive than the group medical coverage which was provided to the Executive by the Company or its affiliates immediately prior to the Termination Date. To the extent applicable and to the extent permitted by law, any continuing coverage provided to the Executive and/or his dependents pursuant to this subparagraph (iii) shall be considered part of, and not in addition to, any coverage required under COBRA. (iv) The Executive will be provided with a lump sum payment of $12,000 for professional outplacement services. Notice by the Company that',
    'vested amounts, if any, to which the Executive is entitled under the Savings Plan as of the Date of Termination, the Company will pay the Executive, in accordance with Section3.04, a lump sum amount equal to the value of the unvested portion, if any, of the employer matching and fixed contributions (and attributable earnings) credited to the Executive under the Savings Plan. 8 -------------------------------------------------------------------------------- (f) Outplacement Services. For a period not to exceed six (6)months following the Date of Termination, the Company will provide the Executive with reasonable outplacement services consistent with past practices of the Company prior to the Change in Control or, if no past practice has been established prior to the Change in Control, consistent with the prevailing practice in the medical device manufacturing industry.',
]
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]

Training Details

Training Dataset

json

Dataset: json
Size: 32,378 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 12 tokens mean: 25.6 tokens max: 47 tokens	min: 7 tokens mean: 190.52 tokens max: 512 tokens	min: 53 tokens mean: 202.25 tokens max: 485 tokens

Samples:

anchor	positive	negative
`query: What is the effective date of the Fifth Amendment to the Approach Resources Inc. 2007 Stock Incentive Plan?`	Exhibit 10.1 FIFTH AMENDMENT TO THE APPROACH RESOURCES INC. 2007 STOCK INCENTIVE PLAN This Fifth Amendment (the “Fifth Amendment”) to the Approach Resources Inc. 2007 Stock Incentive Plan, as amended from time to time (the “Plan”), is made effective as of June 2, 2016 (the “Amendment Effective Date”), by Approach Resources Inc., a Delaware corporation (“Approach”), subject to approval by Approach’s stockholders. W I T N E S S E T H: WHEREAS, Approach established the Plan, originally effective as of June 28, 2007 and most recently amended effective March 2, 2016, under which Approach is authorized to grant equity-based incentive awards to certain employees and service providers of Approach and its subsidiaries; WHEREAS, Section 14.1 of the Plan provides that Approach’s board of directors (the “Board”) may	Exhibit 10.39 AMENDMENT TO THE BPZ RESOURCES, INC. 2007 LONG-TERM INCENTIVE COMPENSATION PLAN WHEREAS, BPZ Resources,Inc. (the “Company”) adopted and maintains the BPZ Energy,Inc. 2007 Long-Term Incentive Compensation Plan (the “Plan”), effective as of June4, 2007, to provide an opportunity for its eligible employees and certain independent contractors to earn long term incentive awards in consideration for their services; WHEREAS, the Company now desires to amend the Plan to reserve additional shares for issuance under the Plan. NOW THEREFORE, effective as of June 20, 2014, the Plan is hereby amended by replacing Section7(a)with the following new Section7(a)that shall read as follows: “(a) Maximum Shares. Subject to adjustment as provided in this Section 7, there is hereby reserved for issuance under the Plan up to 12,000,000 shares of Stock
`query: What is the date on which the Company accepted the subscription?`	to acceptance by the Company, the undersigned has completed this Subscription Agreement to evidence his/her/its subscription for participation in the securities of the Company, this ____th day of _________ 2013. Subscriber Printed name If an entity, on behalf of: Subscriber’s position with entity: The Company has accepted this subscription this ____ day of _________ 2012. OverNear, Inc. By Its: Printed Name: Page11 of 19 Subscription Agreement OverNear, Inc. -------------------------------------------------------------------------------- Subscription Documents - Continued OVERNEAR, INC. (THE “COMPANY”) INVESTOR APPLICATION (QUALIFICATION QUESTIONNAIRE) (CONFIDENTIAL) ALL INFORMATION CONTAINED IN THIS APPLICATIONWILL BE TREATEDCONFIDENTIALLY. The undersigned understands, however, that the Company may present this application to such parties as the Company, in his discretion, deems appropriate when called upon to establish that the proposed offer and sale of the Securities are exempt	and each Subscriber is executing and delivering this agreement in reliance upon the exemption from securities registration afforded by Section 4(2) of the Securities Act and Rule 506 of Regulation D as promulgated by the SEC under the Securities Act; and WHEREAS the subscription for the Securities will be made in accordance with and subject to the terms and conditions of this Subscription Agreement and the Company's Confidential Private Placement Memorandum dated January 28, 2014 together with all amendments thereof and supplements and exhibits thereto and as such may be amended from time to time (the "Memorandum"); and WHEREAS, the Subscriber desires to purchase such number of shares of Common Stock (together with the associated Warrants) as set forth on the signature page hereof on the terms and
`query: What percentage of common shares must an entity own to be considered an Acquiring Person under the Rights Agreement?`	the mutual agreements herein set forth, the parties agree as follows: Section1. Amendment to Section1.1. Section1.1 of the Rights Agreement is amended to read in its entirety as follows: “1.1 “Acquiring Person” shall mean any Person (as such term is hereinafter defined) who or which, together with all Affiliates and Associates (as such terms are hereinafter defined) of such Person, shall be the Beneficial Owner (as such term is hereinafter defined) of 15% or more of the Common Shares of the Company then outstanding, but shall not include: (i) the Company; (ii) any Subsidiary of the Company; (iii) any employee benefit plan of the Company or of any Subsidiary of the Company or any entity holding shares of capital stock of the Company for or pursuant to the	of more than 25% of the Common Shares outstanding immediately prior to the distribution, and in making this determination the Common Shares to be issued to such Person in the distribution shall be deemed to be held by such Person but shall not be included in the aggregate number of outstanding Common Shares immediately prior to the distribution ("Exempt Acquisitions"); the acquisition of Common Shares upon the exercise of Convertible Securities received by such Person pursuant to a Permitted Bid Acquisition, an Exempt Acquisition or a Pro Rata Acquisition (as defined below) ("Convertible Security Acquisitions"); or acquisitions as a result of a stock dividend, a stock split or other event pursuant to which such Person receives or acquires Common Shares or Convertible Securities on the same pro rata

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 1
lr_scheduler_type: cosine_with_restarts
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0001
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: cosine_with_restarts
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: 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: 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
eval_use_gather_object: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs