metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4893
  - loss:TripletLoss
base_model: microsoft/mpnet-base
widget:
  - source_sentence: >-
      I almost envy you your assignment. I see in your mind that you are tempted
      to take my place. Not correct, Doctor, although I am aware of your mind
      attempting to contact mine. Were you born a telepath? Yes. That is why I
      had to study on Vulcan. I understand. May I show you to your
      quarters?[SEP]I think I'll stay here a bit. Ambassador Kollos often finds
      the process of transport somewhat unsettling.
    sentences:
      - ' I don''t see anything, do you?'
      - >-
        I understand. Our ship's surgeon often makes the same complaint. Do call
        when you are ready.
      - Irrelevant, since we are here.
  - source_sentence: >-
      Aye, sir. Aye, sir. Return to your station, Sub-commander. The boarding
      action on the Enterprise will begin with my command. If they resist,
      destroy her. Execution of state criminals is both painful and unpleasant.
      I believe the details are unnecessary. The sentence will be carried out
      immediately after the charges have been recorded. I demand the Right of
      Statement first.[SEP]You understand Romulan tradition well. The right is
      granted.
    sentences:
      - ' No, I... it''s just... you''re just coming off the surgery and you''re not yourself yet and I work for you and Even though last year''s... [Frustrated sigh as House starts smiling smugly.] you''re smiling! I''m saying no and you''re smiling!'
      - I wish to ask a question. What of Sarek's family, his wife and son?
      - >-
        Thank you. I shall not require much time. No more than twenty minutes, I
        should say.
  - source_sentence: >-
      Mankind, ready to kill. That's the way it was in 1881. I wonder how
      humanity managed to survive. We overcame our instinct for violence. Some
      desk-bound Starfleet bureaucrat cut these cloak-and-dagger
      orders.[SEP]Aye, but why the secrecy? This star system's under Federation
      control.
    sentences:
      - >-
        It's in a border area, Mister Scott. The Klingons also claim
        jurisdiction.
      - ' That''s your argument? Better outcome?'
      - Are you all right, Captain?
  - source_sentence: >-
      We're trying to help you, Oxmyx. Nobody helps nobody but himself. Sir, you
      are employing a double negative. Huh? I fail to see why you do not
      understand us. You yourself have stated the need for unity of authority on
      this planet. We agree.[SEP]Yeah, but I got to be the unity.
    sentences:
      - Co-operation, sir, would inevitably result
      - >-
        Quite right, Mister Scott. There's somebody holding us down. All systems
        are go, but we're not moving.
      - ' So today I''m jailbait but in 22 weeks anybody can do anything to me. Will I be so different in 22 weeks?'
  - source_sentence: >-
      What happened? Where have I been? Right here, it seems. But that girl. She
      was so beautiful. So real. Do you remember anything else? No.[SEP]Good.
      Perhaps that explains why he's here. Nothing was real to him except the
      girl.
    sentences:
      - >-
        Sweeping the area of Outpost two. Sensor reading indefinite.
        Double-checking Outpost three. I read dust and debris. Both Earth
        outposts gone, and the asteroids they were constructed on, pulverised.
      - ' It''s killing you.'
      - Captain, the Melkotian object.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on microsoft/mpnet-base
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: evaluator enc
          type: evaluator_enc
        metrics:
          - type: cosine_accuracy
            value: 0.9989781379699707
            name: Cosine Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: evaluator val
          type: evaluator_val
        metrics:
          - type: cosine_accuracy
            value: 0.9930555820465088
            name: Cosine Accuracy

SentenceTransformer based on microsoft/mpnet-base

This is a sentence-transformers model finetuned from microsoft/mpnet-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: microsoft/mpnet-base
Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("greatakela/gennlp_hw1_encoder2025")
# Run inference
sentences = [
    "What happened? Where have I been? Right here, it seems. But that girl. She was so beautiful. So real. Do you remember anything else? No.[SEP]Good. Perhaps that explains why he's here. Nothing was real to him except the girl.",
    'Captain, the Melkotian object.',
    " It's killing you.",
]
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]

Evaluation

Metrics

Triplet

Datasets: evaluator_enc and evaluator_val
Evaluated with TripletEvaluator

Metric	evaluator_enc	evaluator_val
cosine_accuracy	0.999	0.9931

Training Details

Training Dataset

Unnamed Dataset

Size: 4,893 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 2 tokens mean: 90.47 tokens max: 256 tokens	min: 3 tokens mean: 18.64 tokens max: 98 tokens	min: 4 tokens mean: 20.14 tokens max: 199 tokens

Samples:

sentence_0	sentence_1	sentence_2
Oh, well, if that's all. Mister Scott, transport the glommer over to the Klingon ship. Aye, sir. You can't do this to me. Under space salvage laws, he's mine. A planetary surface is not covered by space salvage laws. But if you want the little beastie that bad, Mister Jones, we'll transport you over with it. I withdraw my claim.[SEP]Well, at least we can report the stasis field is not as effective a weapon as we thought. The power drain is too high and takes too long for the Klingon ship to recover to make it practical.	`Agreed, Captain. Tribbles appear to be a much more effective weapon.`	`[protesting] I give him...`
Do you mean that's what the Kelvans really are? Undoubtedly. Well, if they look that way normally, why did they adapt themselves to our bodies? Perhaps practicality. They chose the Enterprise as the best vessel for the trip. Immense beings with a hundred tentacles would have difficulty with the turbolift. We've got to stop them. We outnumber them. Their only hold on us is the paralysis field. Well, that's enough. One wrong move, and they jam all our neural circuits.[SEP]Jam. Spock, if you reverse the circuits on McCoy's neuro-analyser, can you set up a counter field to jam the paralysis projector?	`I'm dubious of the possibilities of success, Captain. The medical equipment is not designed to put out a great deal of power. The polarized elements would burn out quickly.`	`The next step would be a type of brain surgery.`
`Well, speculation isn't much help. We have to get in there. Perhaps there is a way open on the far side. There is much less activity there. That building in the centre. It seems to be important. You stand before the Ruling Tribunal of the Aquans. I am Domar, the High Tribune. I'm Captain Kirk of the starship Enterprise. This is my first officer, Mister Spock.[SEP]You are air-breather enemies from the surface. We have expected spies for a long time.`	`We came here in peace, Tribune.`	`Which is why we need to look at the nerve that you didn't biopsy.`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 3
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: 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: 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

Epoch	Step	Training Loss	evaluator_enc_cosine_accuracy	evaluator_val_cosine_accuracy
-1	-1	-	0.5494	-
0.4902	300	-	0.9808	-
0.8170	500	1.4249	-	-
0.9804	600	-	0.9912	-
1.0	612	-	0.9931	-
1.4706	900	-	0.9963	-
1.6340	1000	0.2269	-	-
1.9608	1200	-	0.9990	-
2.0	1224	-	0.9990	-
2.4510	1500	0.1054	0.9990	-
2.9412	1800	-	0.9990	-
3.0	1836	-	0.9990	-
-1	-1	-	-	0.9931

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.2
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}