|
---
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base_model: google-bert/bert-base-uncased
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datasets:
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- sentence-transformers/gooaq
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language:
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- en
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library_name: sentence-transformers
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license: apache-2.0
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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|
- cosine_accuracy@10
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|
- cosine_precision@1
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|
- cosine_precision@3
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|
- cosine_precision@5
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|
- cosine_precision@10
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- cosine_recall@1
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|
- cosine_recall@3
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|
- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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|
- cosine_mrr@10
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|
- cosine_map@100
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|
- dot_accuracy@1
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|
- dot_accuracy@3
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|
- dot_accuracy@5
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- dot_accuracy@10
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- dot_precision@1
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- dot_precision@3
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- dot_precision@5
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- dot_precision@10
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- dot_recall@1
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|
- dot_recall@3
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- dot_recall@5
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- dot_recall@10
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- dot_ndcg@10
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- dot_mrr@10
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- dot_map@100
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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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:3002496
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- loss:MultipleNegativesRankingLoss
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widget:
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- source_sentence: how to change date format in ms project 2007?
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sentences:
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- '[''Choose File > Options.'', ''Select General.'', ''Under Project view, pick
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an option from the Date format list.'']'
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|
- Cats can be very affectionate and bonded with each other and still bond well and
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|
show affection to their human. Getting two kittens from the same litter, regardless
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|
of gender, can make it easier for them to befriend each other and play—but any
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|
two kittens generally tend to get on well after introductions.
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|
- 'Treat your permed hair like silk or another delicate fabric: washing it once
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a week is enough to keep it clean and help maintain its beauty. Wash your hair
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with warm water. Hot water can strip your hair of oils that help keep it moisturized
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and looking lustrous. Hot water can also ruin the curls.'
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- source_sentence: is the mother in vinegar good for you?
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sentences:
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- Some people say the “mother,” the cloud of yeast and bacteria you might see in
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a bottle of apple cider vinegar, is what makes it healthy. These things are probiotic,
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|
meaning they might give your digestive system a boost, but there isn't enough
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|
research to back up the other claims.
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|
- It is normal for vaginal discharge to increase in amount and become “stringy”
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|
(like egg whites) during the middle of your menstrual cycle when you're ovulating.
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|
If you find that your normal discharge is annoying, you can wear panty liners/shields
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|
on your underwear.
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|
- State law protects cypress trees along Florida's waterways, but it has been up
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|
to the courts to enforce the regulations. ... Landowners can cut down cypress
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|
trees on their land, but trees below the high-water mark are considered state
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|
property and are protected.
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- source_sentence: if you're blocked on whatsapp can you see last seen?
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sentences:
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- Jaguars aren't going to London this year, releases new plan for season tickets.
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The Jaguars will no longer be playing two games in London, and will instead play
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|
both games at TIAA Bank Field.
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|
- Typically, most drugs are absorbed within 20-30 minutes after given by mouth.
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|
Vomiting after this amount of time is not related to the drug in the stomach as
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|
the vast majority, if not all, has already been absorbed.
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|
- You can no longer see a contact's last seen or online in the chat window. Learn
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|
more here. You do not see updates to a contact's profile photo. Any messages sent
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|
to a contact who has blocked you will always show one check mark (message sent),
|
|
and never show a second check mark (message delivered).
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|
- source_sentence: how many enchantments can you put on armor?
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|
sentences:
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- 4 Answers. You can in theory add every enchantment that is compatible with a tool/weapon/armor
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|
onto the same item. The bow can have these 7 enchantments, though mending and
|
|
infinity are mutually exclusive.
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|
- The sleeve length will make or break a jacket. If too long, it will make the jacket
|
|
look too big, and if too short, like you have outgrown your jacket. ... This is
|
|
when you need an experienced tailor, who will be able to shorten the sleeves from
|
|
the shoulders, so the details on the cuffs are not disturbed.
|
|
- Grace period of 60 days granted after the expiration of license for purpose of
|
|
renewal, and license is valid during this period. Renewal of license may occur
|
|
from 60 days (effective August 1, 2016, 180 days) prior to expiration to 3 years
|
|
after date; afterwards, applicant required to take and pass examination.
|
|
- source_sentence: what is the best drugstore shampoo for volume?
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sentences:
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- '[''#8. ... '', ''#7. ... '', ''#6. Hask Biotin Boost Shampoo. ... '', ''#5. Pantene
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Pro-V Sheer Volume Shampoo. ... '', ''#4. John Frieda Luxurious Volume Touchably
|
|
Full Shampoo. ... '', ''#3. Acure Vivacious Volume Peppermint Shampoo. ... '',
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|
''#2. OGX Thick & Full Biotin & Collagen Shampoo. ... '', "#1. L''Oréal Paris
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|
EverPure Sulfate Free Volume Shampoo."]'
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|
- Genes can't control an organism on their own; rather, they must interact with
|
|
and respond to the organism's environment. Some genes are constitutive, or always
|
|
"on," regardless of environmental conditions.
|
|
- In electricity, the phase refers to the distribution of a load. What is the difference
|
|
between single-phase and three-phase power supplies? Single-phase power is a two-wire
|
|
alternating current (ac) power circuit. ... Three-phase power is a three-wire
|
|
ac power circuit with each phase ac signal 120 electrical degrees apart.
|
|
co2_eq_emissions:
|
|
emissions: 523.8395173647017
|
|
energy_consumed: 1.3476635503925931
|
|
source: codecarbon
|
|
training_type: fine-tuning
|
|
on_cloud: false
|
|
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
ram_total_size: 31.777088165283203
|
|
hours_used: 3.544
|
|
hardware_used: 1 x NVIDIA GeForce RTX 3090
|
|
model-index:
|
|
- name: BERT base uncased trained on GooAQ triplets
|
|
results:
|
|
- task:
|
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type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
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name: gooaq dev
|
|
type: gooaq-dev
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.7001
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.8712
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.9219
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.9629
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.7001
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.2904
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.18438000000000002
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.09629000000000001
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.7001
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.8712
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.9219
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.9629
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.8358567622290791
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7945682142857085
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.796615366916047
|
|
name: Cosine Map@100
|
|
- type: dot_accuracy@1
|
|
value: 0.6709
|
|
name: Dot Accuracy@1
|
|
- type: dot_accuracy@3
|
|
value: 0.8558
|
|
name: Dot Accuracy@3
|
|
- type: dot_accuracy@5
|
|
value: 0.9096
|
|
name: Dot Accuracy@5
|
|
- type: dot_accuracy@10
|
|
value: 0.9567
|
|
name: Dot Accuracy@10
|
|
- type: dot_precision@1
|
|
value: 0.6709
|
|
name: Dot Precision@1
|
|
- type: dot_precision@3
|
|
value: 0.28526666666666667
|
|
name: Dot Precision@3
|
|
- type: dot_precision@5
|
|
value: 0.18192000000000003
|
|
name: Dot Precision@5
|
|
- type: dot_precision@10
|
|
value: 0.09567
|
|
name: Dot Precision@10
|
|
- type: dot_recall@1
|
|
value: 0.6709
|
|
name: Dot Recall@1
|
|
- type: dot_recall@3
|
|
value: 0.8558
|
|
name: Dot Recall@3
|
|
- type: dot_recall@5
|
|
value: 0.9096
|
|
name: Dot Recall@5
|
|
- type: dot_recall@10
|
|
value: 0.9567
|
|
name: Dot Recall@10
|
|
- type: dot_ndcg@10
|
|
value: 0.8177950307933399
|
|
name: Dot Ndcg@10
|
|
- type: dot_mrr@10
|
|
value: 0.772776468253962
|
|
name: Dot Mrr@10
|
|
- type: dot_map@100
|
|
value: 0.7751231358698718
|
|
name: Dot Map@100
|
|
---
|
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|
|
# BERT base uncased trained on GooAQ triplets
|
|
|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. 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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|
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## Model Details
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|
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### Model Description
|
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- **Model Type:** Sentence Transformer
|
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- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
|
|
- **Maximum Sequence Length:** 512 tokens
|
|
- **Output Dimensionality:** 768 tokens
|
|
- **Similarity Function:** Cosine Similarity
|
|
- **Training Dataset:**
|
|
- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
|
|
- **Language:** en
|
|
- **License:** apache-2.0
|
|
|
|
### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
|
(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:
|
|
|
|
```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("tomaarsen/bert-base-uncased-gooaq")
|
|
# Run inference
|
|
sentences = [
|
|
'what is the best drugstore shampoo for volume?',
|
|
'[\'#8. ... \', \'#7. ... \', \'#6. Hask Biotin Boost Shampoo. ... \', \'#5. Pantene Pro-V Sheer Volume Shampoo. ... \', \'#4. John Frieda Luxurious Volume Touchably Full Shampoo. ... \', \'#3. Acure Vivacious Volume Peppermint Shampoo. ... \', \'#2. OGX Thick & Full Biotin & Collagen Shampoo. ... \', "#1. L\'Oréal Paris EverPure Sulfate Free Volume Shampoo."]',
|
|
'In electricity, the phase refers to the distribution of a load. What is the difference between single-phase and three-phase power supplies? Single-phase power is a two-wire alternating current (ac) power circuit. ... Three-phase power is a three-wire ac power circuit with each phase ac signal 120 electrical degrees apart.',
|
|
]
|
|
embeddings = model.encode(sentences)
|
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print(embeddings.shape)
|
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# [3, 768]
|
|
|
|
# Get the similarity scores for the embeddings
|
|
similarities = model.similarity(embeddings, embeddings)
|
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print(similarities.shape)
|
|
# [3, 3]
|
|
```
|
|
|
|
<!--
|
|
### Direct Usage (Transformers)
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
|
</details>
|
|
-->
|
|
|
|
<!--
|
|
### Downstream Usage (Sentence Transformers)
|
|
|
|
You can finetune this model on your own dataset.
|
|
|
|
<details><summary>Click to expand</summary>
|
|
|
|
</details>
|
|
-->
|
|
|
|
<!--
|
|
### Out-of-Scope Use
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
|
-->
|
|
|
|
## Evaluation
|
|
|
|
### Metrics
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `gooaq-dev`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
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|
|
| Metric | Value |
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|:--------------------|:-----------|
|
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| cosine_accuracy@1 | 0.7001 |
|
|
| cosine_accuracy@3 | 0.8712 |
|
|
| cosine_accuracy@5 | 0.9219 |
|
|
| cosine_accuracy@10 | 0.9629 |
|
|
| cosine_precision@1 | 0.7001 |
|
|
| cosine_precision@3 | 0.2904 |
|
|
| cosine_precision@5 | 0.1844 |
|
|
| cosine_precision@10 | 0.0963 |
|
|
| cosine_recall@1 | 0.7001 |
|
|
| cosine_recall@3 | 0.8712 |
|
|
| cosine_recall@5 | 0.9219 |
|
|
| cosine_recall@10 | 0.9629 |
|
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| cosine_ndcg@10 | 0.8359 |
|
|
| cosine_mrr@10 | 0.7946 |
|
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| **cosine_map@100** | **0.7966** |
|
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| dot_accuracy@1 | 0.6709 |
|
|
| dot_accuracy@3 | 0.8558 |
|
|
| dot_accuracy@5 | 0.9096 |
|
|
| dot_accuracy@10 | 0.9567 |
|
|
| dot_precision@1 | 0.6709 |
|
|
| dot_precision@3 | 0.2853 |
|
|
| dot_precision@5 | 0.1819 |
|
|
| dot_precision@10 | 0.0957 |
|
|
| dot_recall@1 | 0.6709 |
|
|
| dot_recall@3 | 0.8558 |
|
|
| dot_recall@5 | 0.9096 |
|
|
| dot_recall@10 | 0.9567 |
|
|
| dot_ndcg@10 | 0.8178 |
|
|
| dot_mrr@10 | 0.7728 |
|
|
| dot_map@100 | 0.7751 |
|
|
|
|
<!--
|
|
## Bias, Risks and Limitations
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
|
-->
|
|
|
|
<!--
|
|
### Recommendations
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
|
-->
|
|
|
|
## Training Details
|
|
|
|
### Training Dataset
|
|
|
|
#### sentence-transformers/gooaq
|
|
|
|
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
|
|
* Size: 3,002,496 training samples
|
|
* Columns: <code>question</code> and <code>answer</code>
|
|
* Approximate statistics based on the first 1000 samples:
|
|
| | question | answer |
|
|
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
|
| type | string | string |
|
|
| details | <ul><li>min: 8 tokens</li><li>mean: 11.95 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 60.83 tokens</li><li>max: 130 tokens</li></ul> |
|
|
* Samples:
|
|
| question | answer |
|
|
|:------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
|
| <code>what are the differences between internet and web?</code> | <code>The Internet is a global network of networks while the Web, also referred formally as World Wide Web (www) is collection of information which is accessed via the Internet. Another way to look at this difference is; the Internet is infrastructure while the Web is service on top of that infrastructure.</code> |
|
|
| <code>who is the most important person in a first aid situation?</code> | <code>Subscribe to New First Aid For Free The main principle of incident management is that you are the most important person and your safety comes first! Your first actions when coming across the scene of an incident should be: Check for any dangers to yourself or bystanders. Manage any dangers found (if safe to do so)</code> |
|
|
| <code>why is jibjab not working?</code> | <code>Usually disabling your ad blockers for JibJab will resolve this issue. If you're still having issues loading the card after your ad blockers are disabled, you can try clearing your cache/cookies or updating and restarting your browser. As a last resort, you can try opening JibJab from a different browser.</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"
|
|
}
|
|
```
|
|
|
|
### Evaluation Dataset
|
|
|
|
#### sentence-transformers/gooaq
|
|
|
|
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
|
|
* Size: 10,000 evaluation samples
|
|
* Columns: <code>question</code> and <code>answer</code>
|
|
* Approximate statistics based on the first 1000 samples:
|
|
| | question | answer |
|
|
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
|
| type | string | string |
|
|
| details | <ul><li>min: 8 tokens</li><li>mean: 12.01 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 59.81 tokens</li><li>max: 145 tokens</li></ul> |
|
|
* Samples:
|
|
| question | answer |
|
|
|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
|
| <code>what are some common attributes/characteristics between animal and human?</code> | <code>['Culture.', 'Emotions.', 'Language.', 'Humour.', 'Tool Use.', 'Memory.', 'Self-Awareness.', 'Intelligence.']</code> |
|
|
| <code>is folic acid the same as vitamin b?</code> | <code>Vitamin B9, also called folate or folic acid, is one of 8 B vitamins. All B vitamins help the body convert food (carbohydrates) into fuel (glucose), which is used to produce energy. These B vitamins, often referred to as B-complex vitamins, also help the body use fats and protein.</code> |
|
|
| <code>are bendy buses still in london?</code> | <code>Bendy bus makes final journey for Transport for London. The last of London's bendy buses was taken off the roads on Friday night. ... The final route to be operated with bendy buses has been the 207 between Hayes and White City, and the last of the long vehicles was to run late on Friday.</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"
|
|
}
|
|
```
|
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|
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### Training Hyperparameters
|
|
#### Non-Default Hyperparameters
|
|
|
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- `eval_strategy`: steps
|
|
- `per_device_train_batch_size`: 128
|
|
- `per_device_eval_batch_size`: 128
|
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- `learning_rate`: 2e-05
|
|
- `num_train_epochs`: 1
|
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- `warmup_ratio`: 0.1
|
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- `bf16`: True
|
|
- `batch_sampler`: no_duplicates
|
|
|
|
#### All Hyperparameters
|
|
<details><summary>Click to expand</summary>
|
|
|
|
- `overwrite_output_dir`: False
|
|
- `do_predict`: False
|
|
- `eval_strategy`: steps
|
|
- `prediction_loss_only`: True
|
|
- `per_device_train_batch_size`: 128
|
|
- `per_device_eval_batch_size`: 128
|
|
- `per_gpu_train_batch_size`: None
|
|
- `per_gpu_eval_batch_size`: None
|
|
- `gradient_accumulation_steps`: 1
|
|
- `eval_accumulation_steps`: None
|
|
- `learning_rate`: 2e-05
|
|
- `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`: 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
|
|
- `batch_sampler`: no_duplicates
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
|
</details>
|
|
|
|
### Training Logs
|
|
| Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
|
|
|:------:|:-----:|:-------------:|:------:|:------------------------:|
|
|
| 0 | 0 | - | - | 0.2018 |
|
|
| 0.0000 | 1 | 2.6207 | - | - |
|
|
| 0.0213 | 500 | 0.9092 | - | - |
|
|
| 0.0426 | 1000 | 0.2051 | - | - |
|
|
| 0.0639 | 1500 | 0.1354 | - | - |
|
|
| 0.0853 | 2000 | 0.1089 | 0.0719 | 0.7124 |
|
|
| 0.1066 | 2500 | 0.0916 | - | - |
|
|
| 0.1279 | 3000 | 0.0812 | - | - |
|
|
| 0.1492 | 3500 | 0.0716 | - | - |
|
|
| 0.1705 | 4000 | 0.0658 | 0.0517 | 0.7432 |
|
|
| 0.1918 | 4500 | 0.0623 | - | - |
|
|
| 0.2132 | 5000 | 0.0596 | - | - |
|
|
| 0.2345 | 5500 | 0.0554 | - | - |
|
|
| 0.2558 | 6000 | 0.0504 | 0.0401 | 0.7580 |
|
|
| 0.2771 | 6500 | 0.0498 | - | - |
|
|
| 0.2984 | 7000 | 0.0483 | - | - |
|
|
| 0.3197 | 7500 | 0.0487 | - | - |
|
|
| 0.3410 | 8000 | 0.0458 | 0.0359 | 0.7652 |
|
|
| 0.3624 | 8500 | 0.0435 | - | - |
|
|
| 0.3837 | 9000 | 0.0421 | - | - |
|
|
| 0.4050 | 9500 | 0.0421 | - | - |
|
|
| 0.4263 | 10000 | 0.0405 | 0.0329 | 0.7738 |
|
|
| 0.4476 | 10500 | 0.0392 | - | - |
|
|
| 0.4689 | 11000 | 0.0388 | - | - |
|
|
| 0.4903 | 11500 | 0.0388 | - | - |
|
|
| 0.5116 | 12000 | 0.0361 | 0.0290 | 0.7810 |
|
|
| 0.5329 | 12500 | 0.0362 | - | - |
|
|
| 0.5542 | 13000 | 0.0356 | - | - |
|
|
| 0.5755 | 13500 | 0.0352 | - | - |
|
|
| 0.5968 | 14000 | 0.0349 | 0.0267 | 0.7866 |
|
|
| 0.6182 | 14500 | 0.0334 | - | - |
|
|
| 0.6395 | 15000 | 0.0323 | - | - |
|
|
| 0.6608 | 15500 | 0.0325 | - | - |
|
|
| 0.6821 | 16000 | 0.0316 | 0.0256 | 0.7879 |
|
|
| 0.7034 | 16500 | 0.0313 | - | - |
|
|
| 0.7247 | 17000 | 0.0306 | - | - |
|
|
| 0.7460 | 17500 | 0.0328 | - | - |
|
|
| 0.7674 | 18000 | 0.0303 | 0.0238 | 0.7928 |
|
|
| 0.7887 | 18500 | 0.0301 | - | - |
|
|
| 0.8100 | 19000 | 0.0291 | - | - |
|
|
| 0.8313 | 19500 | 0.0286 | - | - |
|
|
| 0.8526 | 20000 | 0.0295 | 0.0218 | 0.7952 |
|
|
| 0.8739 | 20500 | 0.0288 | - | - |
|
|
| 0.8953 | 21000 | 0.0277 | - | - |
|
|
| 0.9166 | 21500 | 0.0266 | - | - |
|
|
| 0.9379 | 22000 | 0.0289 | 0.0218 | 0.7971 |
|
|
| 0.9592 | 22500 | 0.0286 | - | - |
|
|
| 0.9805 | 23000 | 0.0275 | - | - |
|
|
| 1.0 | 23457 | - | - | 0.7966 |
|
|
|
|
|
|
### Environmental Impact
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
|
- **Energy Consumed**: 1.348 kWh
|
|
- **Carbon Emitted**: 0.524 kg of CO2
|
|
- **Hours Used**: 3.544 hours
|
|
|
|
### Training Hardware
|
|
- **On Cloud**: No
|
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
- **RAM Size**: 31.78 GB
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.6
|
|
- Sentence Transformers: 3.1.0.dev0
|
|
- Transformers: 4.41.2
|
|
- PyTorch: 2.3.0+cu121
|
|
- Accelerate: 0.31.0
|
|
- 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",
|
|
}
|
|
```
|
|
|
|
#### 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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