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README.md
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---
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base_model: aubmindlab/bert-base-arabertv02
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datasets: []
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language: [
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:
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- loss:MatryoshkaLoss
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- loss:MultipleNegativesRankingLoss
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02).
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It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search,
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paraphrase mining, text classification, clustering, and more.
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The model is based on a sample from the `akhooli/arabic-triplets-1m-curated-sims-len` dataset. This is an early test version. Do not use while the model name has the
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word `test`.
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## Model Details
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'
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'
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'
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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#### Unnamed Dataset
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* Size:
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor
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| type | string
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| details | <ul><li>min: 4 tokens</li><li>mean:
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* Samples:
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| anchor
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| <code
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| <code
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| <code
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
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```json
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{
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#### Unnamed Dataset
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* Size:
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor | positive
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-
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| type | string | string
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| details | <ul><li>min: 4 tokens</li><li>mean:
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* Samples:
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| anchor
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| <code
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| <code
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| <code
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
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```json
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{
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `learning_rate`: 2e-05
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- `warmup_ratio`: 0.1
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- `fp16`: True
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- `batch_sampler`: no_duplicates
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`:
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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</details>
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### Training Logs
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| Epoch | Step
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### Framework Versions
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---
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base_model: aubmindlab/bert-base-arabertv02
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datasets: []
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language: []
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:75000
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- loss:MatryoshkaLoss
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- loss:MultipleNegativesRankingLoss
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widget:
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- source_sentence: ุฑุฌู ููุธุฑ ุฅูู ู
ุง ูุจุฏู ุฃูู ูุทุน ู
ู ุงููุฑู ุงูู
ููู ูุงู
ุฑุฃุฉ ูู ุงูู
ุทุจุฎ.
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sentences:
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- ุฒูุฌ ูุฒูุฌุชู ูุชุฒูุฌุงู ุนูู ุงูุฌุจุงู ุงูุณููุณุฑูุฉ
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- ู
ุง ูู ุงููุชุงุจ ุงูุฌูุฏ ูููุฑุงุกุฉุ
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- ุฑุฌู ูุญุฏู ูู ุงู
ุฑุฃุฉ ูู ุงูู
ุทุจุฎ
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- source_sentence: ุงูููุจ ุงูุฑู
ุงุฏู ูุฑูุถ ุนูู ุฌุงูุจ ุจุฑูุฉ ุจููู
ุง ุงูููุจ ุงูุฃุตูุฑ ูููุฒ ุฅูู ุงูุจุฑูุฉ.
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sentences:
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- ุงูููุงุจ ุชุฃูู ุนุดุงุฆูุง ุงููููู
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- ููุงู ููุจุงู ุจุงูุฎุงุฑุฌ ุจุงููุฑุจ ู
ู ุญู
ุงู
ุงูุณุจุงุญุฉ
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- ููู ุชุตูุน ุฒุฌุงุฌ ุจูุฑููุณุ
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- source_sentence: ููู ูู
ูููุง ูุณุจ ุงูู
ุงู ู
ู ููุชููุจุ
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sentences:
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- ููู ูู
ูููู ูุณุจ ุงูู
ุงู ู
ู ุฎูุงู ุงูููุชููุจุ
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- ูุชู ูุฑู
ู ุญููุจุฉ.
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- ูู ูู
ูู ูุดุฎุต ู
ุชุญูู ุฌูุณูุงู ุฃู ูุนูุฏ ุฅูู ุฌูุณู ุงูุณุงุจู ุจุนุฏ ุฌุฑุงุญุฉ ุชุบููุฑ ุงูุฌูุณุ
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- source_sentence: ููู ูุญุตู ุงูู
ุฑุก ุนูู ุฑูู
ูุงุชู ูุชุงุฉ ุจุณุฑุนุฉุ
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sentences:
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- ุงู
ุฑุฃุฉ ุชุชุณูู ูู ุณูู ุงูู
ุฒุงุฑุนูู
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- ููู ุชุญุตู ุนูู ุฑูู
ูุงุชู ูุชุงุฉุ
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- ููู ูู
ูููู ุงูุชุฎูุต ู
ู ุญุจ ุงูุดุจุงุจุ
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- source_sentence: ู
ุง ูู ููุน ุงูุฏููู ุงูู
ูุฌูุฏุฉ ูู ุงูุฃูููุงุฏู
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sentences:
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- ุญูุงูู 15 ูู ุงูู
ุงุฆุฉ ู
ู ุงูุฏููู ูู ุงูุฃูููุงุฏู ู
ุดุจุนุฉ ุ ู
ุน ูู ููุจ ูุงุญุฏ ู
ู ุงูุฃูููุงุฏู
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ุงูู
ูุฑูู
ูุญุชูู ุนูู 3.2 ุฌุฑุงู
ู
ู ุงูุฏููู ุงูู
ุดุจุนุฉ ุ ููู ู
ุง ูู
ุซู 16 ูู ุงูู
ุงุฆุฉ ู
ู DV
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+
ุงูุจุงูุบ 20 ุฌุฑุงู
ูุง. ุชุญุชูู ุงูุฃูููุงุฏู ูู ุงูุบุงูุจ ุนูู ุฏููู ุฃุญุงุฏูุฉ ุบูุฑ ู
ุดุจุนุฉ ุ ู
ุน 67
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ูู ุงูู
ุงุฆุฉ ู
ู ุฅุฌู
ุงูู ุงูุฏููู ุ ุฃู 14.7 ุฌุฑุงู
ูุง ููู ููุจ ู
ูุฑูู
ุ ููุชููู ู
ู ูุฐุง ุงูููุน
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ู
ู ุงูุฏููู.
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- ุงู
ุฑุฃุฉ ุชุณุชู
ุชุน ุจุฑุงุฆุญุฉ ุดุงููุง ูู ุงูููุงุก ุงูุทูู.
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+
- ูู
ูู ุฃู ูุคุฏู ุงุฑุชูุงุน ู
ุณุชูู ุงูุฏููู ุงูุซูุงุซูุฉ ุ ููู ููุน ู
ู ุงูุฏููู (ุงูุฏููู) ูู ุงูุฏู
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ุ ุฅูู ุฒูุงุฏุฉ ุฎุทุฑ ุงูุฅุตุงุจุฉ ุจุฃู
ุฑุงุถ ุงูููุจ ุ ููู
ูู ุฃู ูุคุฏู ุชูููุฑ ู
ุณุชูู ู
ุฑุชูุน ู
ู ุงูุฏููู
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ุงูุซูุงุซูุฉ ุ ููู ููุน ู
ู ุงูุฏููู (ุงูุฏููู) ูู ุงูุฏู
ุ ุฅูู ุฒูุงุฏุฉ ุฎุทุฑ ุงูุฅุตุงุจุฉ ุจุฃู
ุฑุงุถ ุงูููุจ.
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ู
ุฑุถ.
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---
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# SentenceTransformer based on aubmindlab/bert-base-arabertv02
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02). 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.
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## Model Details
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'ู
ุง ูู ููุน ุงูุฏููู ุงูู
ูุฌูุฏุฉ ูู ุงูุฃูููุงุฏู',
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'ุญูุงูู 15 ูู ุงูู
ุงุฆุฉ ู
ู ุงูุฏููู ูู ุงูุฃูููุงุฏู ู
ุดุจุนุฉ ุ ู
ุน ูู ููุจ ูุงุญุฏ ู
ู ุงูุฃูููุงุฏู ุงูู
ูุฑูู
ูุญุชูู ุนูู 3.2 ุฌุฑุงู
ู
ู ุงูุฏููู ุงูู
ุดุจุนุฉ ุ ููู ู
ุง ูู
ุซู 16 ูู ุงูู
ุงุฆุฉ ู
ู DV ุงูุจุงูุบ 20 ุฌุฑุงู
ูุง. ุชุญุชูู ุงูุฃูููุงุฏู ูู ุงูุบุงูุจ ุนูู ุฏููู ุฃุญุงุฏูุฉ ุบูุฑ ู
ุดุจุนุฉ ุ ู
ุน 67 ูู ุงูู
ุงุฆุฉ ู
ู ุฅุฌู
ุงูู ุงูุฏููู ุ ุฃู 14.7 ุฌุฑุงู
ูุง ููู ููุจ ู
ูุฑูู
ุ ููุชููู ู
ู ูุฐุง ุงูููุน ู
ู ุงูุฏููู.',
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'ูู
ูู ุฃู ูุคุฏู ุงุฑุชูุงุน ู
ุณุชูู ุงูุฏููู ุงูุซูุงุซูุฉ ุ ููู ููุน ู
ู ุงูุฏููู (ุงูุฏููู) ูู ุงูุฏู
ุ ุฅูู ุฒูุงุฏุฉ ุฎุทุฑ ุงูุฅุตุงุจุฉ ุจุฃู
ุฑุงุถ ุงูููุจ ุ ููู
ูู ุฃู ูุคุฏู ุชูููุฑ ู
ุณุชูู ู
ุฑุชูุน ู
ู ุงูุฏููู ุงูุซูุงุซูุฉ ุ ููู ููุน ู
ู ุงูุฏููู (ุงูุฏููู) ูู ุงูุฏู
ุ ุฅูู ุฒูุงุฏุฉ ุฎุทุฑ ุงูุฅุตุงุจุฉ ุจุฃู
ุฑุงุถ ุงูููุจ. ู
ุฑุถ.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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|
|
| 153 |
#### Unnamed Dataset
|
| 154 |
|
| 155 |
|
| 156 |
+
* Size: 75,000 training samples
|
| 157 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 158 |
* Approximate statistics based on the first 1000 samples:
|
| 159 |
+
| | anchor | positive | negative |
|
| 160 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
| 161 |
+
| type | string | string | string |
|
| 162 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 12.88 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.74 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.38 tokens</li><li>max: 146 tokens</li></ul> |
|
| 163 |
* Samples:
|
| 164 |
+
| anchor | positive | negative |
|
| 165 |
+
|:------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:--------------------------------------------------|
|
| 166 |
+
| <code>ูู ุชุดุงุฌุฑ (ุณู ุฅุณ ูููุณ) ู (ุฌู ุขุฑ ุขุฑ ุชููููู) ุ ุฅู ูุงู ุงูุฃู
ุฑ ูุฐููุ ูู
ุง ูู ุงูุณุจุจุ</code> | <code>ูู ุตุญูุญ ุฃู (ุณู ุฅุณ ูููุณ) ู (ุชููููู) ุชุดุงุฌุฑุงุ</code> | <code>ู
ุง ูู ุฃูุถู ุงููุชุจ ููุฏุฑุงุณุฉ ูู ุงูุฌุงู
ุนุฉุ</code> |
|
| 167 |
+
| <code>ู
ุง ูู ุงุนุฑุงุถ ููุฑ ุงูุฏู
ุ</code> | <code>ู
ุง ูู ุงุนุฑุงุถ ุงูุงููู
ูุงุ</code> | <code>ููู ุงุญุถุฑ ูููุฉ ุงูุนุณูุ</code> |
|
| 168 |
+
| <code>ู
ู ุณุชุตูุช ููุ ุฏููุงูุฏ ุชุฑุงู
ุจ ุฃู
ูููุงุฑู ููููุชููุ</code> | <code>ูู ุชุคูุฏูู ุฏููุงูุฏ ุชุฑุงู
ุจ ุฃู
ูููุงุฑู ููููุชููุ ูู
ุงุฐุงุ</code> | <code>ููู ุฃุชุบูุจ ุนูู ุฅุฏู
ุงู ุงูู
ูุงุฏ ุงูุฅุจุงุญูุฉุ</code> |
|
| 169 |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 170 |
```json
|
| 171 |
{
|
|
|
|
| 193 |
#### Unnamed Dataset
|
| 194 |
|
| 195 |
|
| 196 |
+
* Size: 25,000 evaluation samples
|
| 197 |
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
|
| 198 |
* Approximate statistics based on the first 1000 samples:
|
| 199 |
+
| | anchor | positive | negative |
|
| 200 |
+
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
| 201 |
+
| type | string | string | string |
|
| 202 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 12.6 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.82 tokens</li><li>max: 239 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.78 tokens</li><li>max: 128 tokens</li></ul> |
|
| 203 |
* Samples:
|
| 204 |
+
| anchor | positive | negative |
|
| 205 |
+
|:-----------------------------------------------------------|:-------------------------------------------------------------|:--------------------------------------------|
|
| 206 |
+
| <code>ูุนู
, ูุนู
, ุฃู ุฑุฃูุช " ุชุดูู
ุง ุจุงุฑุง ุฏูุณู "</code> | <code>ูุนู
ุ ุฃู "ุชุดูู
ุง ุจุงุฑุง ุฏูุณู" ูุงูุช ุชูู ุงูุชู ุดุงูุฏุชูุง</code> | <code>ุฃูุง ูู
ุฃุฑู "ุชุดูู
ุง ุจุงุฑุง ุฏูุณู".</code> |
|
| 207 |
+
| <code>ุฑุฌู ูุงู
ุฑุฃุฉ ูุฌูุณุงู ุนูู ุงูุดุงุทุฆ ุจููู
ุง ุชุบุฑุจ ุงูุดู
ุณ</code> | <code>ููุงู ุฑุฌู ูุงู
ุฑุฃุฉ ูุฌูุณุงู ุนูู ุงูุดุงุทุฆ</code> | <code>ุฅููู
ูุดุงูุฏูู ุดุฑูู ุงูุดู
ุณ</code> |
|
| 208 |
+
| <code>ููู ุฃุณูุทุฑ ุนูู ุบุถุจูุ</code> | <code>ู
ุง ูู ุฃูุถู ุทุฑููุฉ ููุณูุทุฑุฉ ุนูู ุงูุบุถุจุ</code> | <code>ููู ุฃุนุฑู ุฅู ูุงูุช ุฒูุฌุชู ุชุฎููููุ</code> |
|
| 209 |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 210 |
```json
|
| 211 |
{
|
|
|
|
| 235 |
- `per_device_train_batch_size`: 16
|
| 236 |
- `per_device_eval_batch_size`: 16
|
| 237 |
- `learning_rate`: 2e-05
|
| 238 |
+
- `num_train_epochs`: 5
|
| 239 |
- `warmup_ratio`: 0.1
|
| 240 |
- `fp16`: True
|
| 241 |
- `batch_sampler`: no_duplicates
|
|
|
|
| 259 |
- `adam_beta2`: 0.999
|
| 260 |
- `adam_epsilon`: 1e-08
|
| 261 |
- `max_grad_norm`: 1.0
|
| 262 |
+
- `num_train_epochs`: 5
|
| 263 |
- `max_steps`: -1
|
| 264 |
- `lr_scheduler_type`: linear
|
| 265 |
- `lr_scheduler_kwargs`: {}
|
|
|
|
| 356 |
</details>
|
| 357 |
|
| 358 |
### Training Logs
|
| 359 |
+
| Epoch | Step | Training Loss | loss |
|
| 360 |
+
|:------:|:-----:|:-------------:|:------:|
|
| 361 |
+
| 0.2133 | 500 | 1.4163 | 0.3134 |
|
| 362 |
+
| 0.4266 | 1000 | 0.3306 | 0.1912 |
|
| 363 |
+
| 0.6399 | 1500 | 0.2263 | 0.1527 |
|
| 364 |
+
| 0.8532 | 2000 | 0.1818 | 0.1297 |
|
| 365 |
+
| 1.0666 | 2500 | 0.1658 | 0.1167 |
|
| 366 |
+
| 1.2799 | 3000 | 0.1139 | 0.1040 |
|
| 367 |
+
| 1.4932 | 3500 | 0.0808 | 0.1018 |
|
| 368 |
+
| 1.7065 | 4000 | 0.0692 | 0.0959 |
|
| 369 |
+
| 1.9198 | 4500 | 0.058 | 0.0958 |
|
| 370 |
+
| 2.1331 | 5000 | 0.0653 | 0.0882 |
|
| 371 |
+
| 2.3464 | 5500 | 0.0503 | 0.0912 |
|
| 372 |
+
| 2.5597 | 6000 | 0.0338 | 0.0970 |
|
| 373 |
+
| 2.7730 | 6500 | 0.0363 | 0.0906 |
|
| 374 |
+
| 2.9863 | 7000 | 0.0375 | 0.0856 |
|
| 375 |
+
| 3.1997 | 7500 | 0.0401 | 0.0879 |
|
| 376 |
+
| 3.4130 | 8000 | 0.031 | 0.0848 |
|
| 377 |
+
| 3.6263 | 8500 | 0.0255 | 0.0938 |
|
| 378 |
+
| 3.8396 | 9000 | 0.0239 | 0.0858 |
|
| 379 |
+
| 4.0529 | 9500 | 0.0305 | 0.0840 |
|
| 380 |
+
| 4.2662 | 10000 | 0.0281 | 0.0833 |
|
| 381 |
+
| 4.4795 | 10500 | 0.0174 | 0.0840 |
|
| 382 |
+
| 4.6928 | 11000 | 0.0216 | 0.0882 |
|
| 383 |
+
| 4.9061 | 11500 | 0.022 | 0.0866 |
|
| 384 |
|
| 385 |
|
| 386 |
### Framework Versions
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 540795752
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ee308a99b75411cbc36588efb0b0a39c698668b9d5a9cdf2afd8fcd82bdb2f44
|
| 3 |
size 540795752
|