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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:268
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Birdwatching for Beginners with Barbara Hannah Grufferman
sentences:
- Bird that breeds in the Arctic and sub-Arctic and migrates to the Antarctic
- '[birds chirping] [Discover Bird-Watching
with Barbara Hannah Grufferman] [♪ music and birds chirping ♪] We are in the park
and I''m meeting up
with Birder Bob, who''s an expert on birding. >> It''s so nice to meet you.
>> Yes. So, listen. I came prepared.
I have my backpack. >> I even have a little notepad in there.
>> All right. >> But what am I missing?
>> Ah! Binoculars. >> May I place these over your head?
>> Please do! Thank you. [laughing]
Let''s go! Vamos! So birding is becoming
the fastest-growing outdoor activity— ["Birding Bob" DeCandido, Ornithologist]
>> Yes.
>> —in the country. >> Why do you think that is? Why?
>> Well, you can watch birds from inside looking outside
at a bird feeder in your backyard, but you can also go to a local park. [♪ music
♪]
Here we are in this giant woodland in the middle of the city. >> And it''s beautiful.
>> Yeah. And we''re getting the clean air. We''re looking up.
We can see birds up there. >> We can hear the cardinals singing.
>> Here''s one. They''re migrating north along a flyway here. >> What''s a flyway?
>> Oh my goodness! A flyway is like an aerial path for birds, and oftentimes it''s
tied to a coastline
or a mountain chain. So there are some very common birds around
that are easy to recognize. Here he is.
Here''s your red-bellied woodpecker right here. I''m going to use my binoculars.
[laughing] Ah! It seems to me that with birding you could just—depending upon
weather— put on a sweater, a jacket, whatever and get out there and walk and look
and you''ll be birding. >> Yes.
>> Is it more complicated than that? Do I need more equipment? If you want to
take it to the next level,
a pair of inexpensive binoculars and a book so you have a reference to go with.
It''s like a guidebook to birds. Yes, because this is your classroom, you know?
>> Right.
>> And if you can teach yourself, all the best way in the world to learn. [♪ music
♪] I''m going to do some special sounds. This is called pishing, which is
[demonstrating pishing] There comes somebody on the left. Now, it seems counterintuitive
that you make sounds and birds come to the sound. >> Yes.
>> But birds come in because they operate as a team. [demonstrating pishing]
What a wonderful thing! Yes, yeah.
[pishing] >> Look, here comes something.
>> You never know what you''re going to find
as you turn a corner. And all you need
is your eyes and ears and curiosity. Should I go closer? [birds chirping] Oh!
[bird chirping] Hello, little cutie! [birds chirping] They like my chia energy
bars.
[laughing] [♪ music and birds chirping ♪] [♪ music and birds chirping ♪] This
is such a great way to get outside,
move your body, and be with nature. Bird-watching is a great way
to see the local area and then take it national. I loved my birding experience
today. It’s—a new world
has been opened up for me really. So I think as of today
I can call myself an official birder. [AARP, Real Possibilities]'
- Teal is a dark cyan color. Its name comes from that of a bird, the Eurasian teal
which has a similarly colored stripe on its head. The word is often used colloquially
to refer to shades of cyan in general.
- source_sentence: Corn bunting
sentences:
- The corn bunting is a passerine bird in the bunting family Emberizidae, a group
now separated by most modern authors from the finches, Fringillidae. This is a
large bunting with heavily streaked buff-brown plumage. The sexes are similar
but the male is slightly larger than the female. Its range extends from Western
Europe and North Africa across to northwestern China.
- The alpine swift is a species of swift found in Africa, southern Europe, and Asia.
They breed in mountains from southern Europe to the Himalayas. Like common swifts,
they are migratory; the southern European population winters further south in
southern Africa. They have very short legs which are used for clinging to vertical
surfaces. Like most swifts, they never settle voluntarily on the ground, spending
most of their lives in the air living on the insects they catch in their beaks.
- The little tern is a seabird of the family Laridae. It was first described by
the German naturalist Peter Simon Pallas in 1764 and given the binomial name Sterna
albifrons. It was moved to the genus Sternula when the genus Sterna was restricted
to the larger typical terns. The genus name Sternula is a diminutive of Sterna,
'tern', while the specific name albifrons is from Latin albus, 'white', and frons,
'forehead'.
- source_sentence: Lesser spotted woodpecker
sentences:
- The Mediterranean gull is a small gull. The scientific name is from Ancient Greek.
The genus Ichthyaetus is from ikhthus, "fish", and aetos, "eagle", and the specific
melanocephalus is from melas, "black", and -kephalos "-headed".
- The spotted flycatcher is a small passerine bird in the Old World flycatcher family.
It breeds in most of Europe and in the Palearctic to Siberia, and is migratory,
wintering in Africa and south western Asia. It is declining in parts of its range.
- The lesser spotted woodpecker is a member of the woodpecker family Picidae. It
was formerly assigned to the genus Dendrocopos. Some taxonomic authorities continue
to list the species there.
- source_sentence: Barnacle goose
sentences:
- The short-toed treecreeper is a small passerine bird found in woodlands through
much of the warmer regions of Europe and into north Africa. It has a generally
more southerly distribution than the other European treecreeper species, the common
treecreeper, with which it is easily confused where they both occur. The short-toed
treecreeper tends to prefer deciduous trees and lower altitudes than its relative
in these overlap areas. Although mainly sedentary, vagrants have occurred outside
the breeding range.
- The barnacle goose is a species of goose that belongs to the genus Branta of black
geese, which contains species with extensive black in the plumage, distinguishing
them from the grey Anser species. Despite its superficial similarity to the brant
goose, genetic analysis has shown its closest relative is the cackling goose.
- The grey plover or black-bellied plover is a large plover breeding in Arctic regions.
It is a long-distance migrant, with a nearly worldwide coastal distribution when
not breeding.
- source_sentence: White stork
sentences:
- The long-tailed duck is a medium-sized sea duck that breeds in the tundra and
taiga regions of the arctic and winters along the northern coastlines of the Atlantic
and Pacific Oceans. It is the only member of the genus Clangula.
- The white stork is a large bird in the stork family, Ciconiidae. Its plumage is
mainly white, with black on the bird's wings. Adults have long red legs and long
pointed red beaks, and measure on average 100–115 cm (39–45 in) from beak tip
to end of tail, with a 155–215 cm (61–85 in) wingspan. The two subspecies, which
differ slightly in size, breed in Europe north to Finland, northwestern Africa,
Palearctic east to southern Kazakhstan and southern Africa. The white stork is
a long-distance migrant, wintering in Africa from tropical Sub-Saharan Africa
to as far south as South Africa, or on the Indian subcontinent. When migrating
between Europe and Africa, it avoids crossing the Mediterranean Sea and detours
via the Levant in the east or the Strait of Gibraltar in the west, because the
air thermals on which it depends for soaring do not form over water.
- 'The shovelers are four species of dabbling ducks in the genus Spatula with long,
broad spatula-shaped beaks:Red shoveler Spatula platalea
Cape shoveler Spatula smithii
Australasian shoveler Spatula rhynchotis
Northern shoveler Spatula clypeata'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, '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:
```bash
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("Nicolas-Spettel/bird-qa-model")
# Run inference
sentences = [
'White stork',
"The white stork is a large bird in the stork family, Ciconiidae. Its plumage is mainly white, with black on the bird's wings. Adults have long red legs and long pointed red beaks, and measure on average 100–115\xa0cm (39–45\xa0in) from beak tip to end of tail, with a 155–215\xa0cm (61–85\xa0in) wingspan. The two subspecies, which differ slightly in size, breed in Europe north to Finland, northwestern Africa, Palearctic east to southern Kazakhstan and southern Africa. The white stork is a long-distance migrant, wintering in Africa from tropical Sub-Saharan Africa to as far south as South Africa, or on the Indian subcontinent. When migrating between Europe and Africa, it avoids crossing the Mediterranean Sea and detours via the Levant in the east or the Strait of Gibraltar in the west, because the air thermals on which it depends for soaring do not form over water.",
'The long-tailed duck is a medium-sized sea duck that breeds in the tundra and taiga regions of the arctic and winters along the northern coastlines of the Atlantic and Pacific Oceans. It is the only member of the genus Clangula.',
]
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, 0.7092, 0.0837],
# [0.7092, 1.0000, 0.1957],
# [0.0837, 0.1957, 1.0000]])
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 268 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 268 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.63 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 94.23 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Corn bunting</code> | <code>The corn bunting is a passerine bird in the bunting family Emberizidae, a group now separated by most modern authors from the finches, Fringillidae. This is a large bunting with heavily streaked buff-brown plumage. The sexes are similar but the male is slightly larger than the female. Its range extends from Western Europe and North Africa across to northwestern China.</code> |
| <code>Water pipit</code> | <code>The water pipit is a small passerine bird which breeds in the mountains of Southern Europe and the Palearctic eastwards to China. It is a short-distance migrant; many birds move to lower altitudes or wet open lowlands in winter.</code> |
| <code>Marsh tit</code> | <code>The marsh tit is a Eurasian passerine bird in the tit family Paridae and genus Poecile, closely related to the willow tit, Père David's and Songar tits. It is a small bird, around 12 cm (4.7 in) long and weighing 12 g (0.42 oz), with a black crown and nape, pale cheeks, brown back and greyish-brown wings and tail. Between 8 and 11 subspecies are recognised. Its close resemblance to the willow tit can cause identification problems, especially in the United Kingdom where the local subspecies of the two are very similar: they were not recognised as separate species until 1897.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 2
- `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}
- `parallelism_config`: 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
- `hub_revision`: None
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Framework Versions
- Python: 3.13.7
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cpu
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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