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--- |
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base_model: Snowflake/snowflake-arctic-embed-xs |
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datasets: [] |
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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 |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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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:100000 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: 'Represent this sentence for searching relevant passages: what |
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is spider silk made of' |
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sentences: |
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- Arachnid Pictures. Spider's silk is made up of chains of amino acids. In other |
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words, it is simply a protein (see How Food Works for details on amino acids and |
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proteins). The two primary amino acids are glycine and alanine. Spider silk is |
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extremely strong -- it is about five times stronger than steel and twice as strong |
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as Kevlar of the same weight. |
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- Spider silk is made of several kinds of proteins. These proteins are processed |
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and stored within the spider's abdomen. Spiders typically have six or eight spinnerets, |
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organs at the rear of their abdomen, through which they extrude the silk proteins. |
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- Neon is the second lightest noble gas, after helium, and it has an atomic number |
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of 10. On the periodic table, it is identified with the symbol Ne. The noble gases |
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were recognized in the late 1800s, when scientists realized that an entire class |
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of gases was missing from the periodic table of elements. |
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- source_sentence: 'Represent this sentence for searching relevant passages: what |
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is a caring community of learners' |
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sentences: |
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- 'A couple of my friends and I made hot ice for our school science fair. We used |
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sodium acetate. This project won first place at my schools science fair!!! Materials: |
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Stove. Pot. A spoon. A glass cup. and Sodium acetate (you can find it online or |
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in certain heat packs). How to do it: You heat water in a pot and put the sodium |
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acetate in the water.' |
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- Caring Community of Learners. A group or classroom in which children and adults |
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engage in warm, positive relationships; treat each other with respect; and learn |
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from and with each other. Self-concept. Children's stable perceptions about themselves |
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despite variations in their behavior. |
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- 'Transcript of Creating a Caring Community of Caring Learners: - Caring Community |
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of Learners: Group or classroom in which children and adults have positive, respectful |
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relationships and learn from each other. - attachment theory: children''s ability |
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to learn depends on trusting relationships with caregivers.' |
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- source_sentence: 'Represent this sentence for searching relevant passages: what |
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does dark circles around deep set eyes mean' |
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sentences: |
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- Production Planner, Manufacturing Salary. (United States). The average salary |
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for a Production Planner, Manufacturing is $51,962 per year. A skill in SAP Enterprise |
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Resource Planning (ERP) is associated with high pay for this job. People in this |
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job generally don't have more than 20 years' experience. |
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- Symptoms & Signs. Dark circles under the eyes are a common complaint of both men |
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and women, although they can occasionally be seen in children. As people age, |
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the skin becomes thinner and collagen is lost, sometimes enhancing the appearance |
|
of blood vessels beneath the eyes and making the area appear darker. |
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- What are dark circles under the eyes? Dark circles under the eyes, sometimes called |
|
shadows or dark rings under the eyes, are the appearance of dark skin between |
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the lower eyelid and the top of the cheek. Dark circles under the eyes can occur |
|
in infants, children, adolescents and adults, and to men and women alike. It is |
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commonly assumed that dark circles under the eyes are caused by a lack of sleep, |
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and poor quality sleep and insomnia can certainly cause this condition. |
|
- source_sentence: 'Represent this sentence for searching relevant passages: how big |
|
is rv access' |
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sentences: |
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- The average length of bigger RVs is between 7.6 meters to 12 meters or 25 feet |
|
to 40 feet. These vehicles are usually packed with different interesting features, |
|
most of which are intended to offer luxury and convenience. |
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- Murder, My Sweet (released as Farewell, My Lovely in the United Kingdom) is a |
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1944 American film noir, directed by Edward Dmytryk and starring Dick Powell, |
|
Claire Trevor, and Anne Shirley. The film is based on Raymond Chandler 's 1940 |
|
novel Farewell, My Lovely. A second film adaptation of the novel was made in 1975 |
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and released under Chandler's title. Murder, My Sweet turned out to be Anne Shirley's |
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final film. She retired from acting in 1944 at age 26. |
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- It should be wider then the rv.....lol sorry could not pass that one up. A standard |
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RV is normally around 96 inches wide at most, newer larger class A's are around |
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102 inches wide. This width does not include mirrors or other safety equipment..... |
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Last edited by rtandc; 10-22-2010 at 05:41 AM.. |
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- source_sentence: 'Represent this sentence for searching relevant passages: how many |
|
pitchers used per game' |
|
sentences: |
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- Trackback - A method by which a blogger receives notification which other bloggers |
|
link to his or her blog entry |
|
- Accroding to the statistics in the Baseball Reference page showing 2014 Major |
|
League Baseball Pitching Pitches, 745 pitchers threw 704,983 pitches in 2430 games |
|
for an average of 290 pitches per game. |
|
- In modern day baseball, teams generally have five starting pitchers, and they |
|
take it in turn to start a game every fifth day (hence the phrase rotation). Sometimes, |
|
if the schedule pans out, a team can get away with a four man rotation, and in |
|
the distant past some teams managed a three man rotation. |
|
model-index: |
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- name: Fine-tuned snowflake actic xs based on MS-Marco triplets |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: xs msmarco triplet |
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type: xs-msmarco-triplet |
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metrics: |
|
- type: cosine_accuracy |
|
value: 0.571 |
|
name: Cosine Accuracy |
|
- type: dot_accuracy |
|
value: 0.4286 |
|
name: Dot Accuracy |
|
- type: manhattan_accuracy |
|
value: 0.5728 |
|
name: Manhattan Accuracy |
|
- type: euclidean_accuracy |
|
value: 0.571 |
|
name: Euclidean Accuracy |
|
- type: max_accuracy |
|
value: 0.5728 |
|
name: Max Accuracy |
|
- task: |
|
type: triplet |
|
name: Triplet |
|
dataset: |
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name: xs msmarco triplet train |
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type: xs-msmarco-triplet-train |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.5696 |
|
name: Cosine Accuracy |
|
- type: dot_accuracy |
|
value: 0.43 |
|
name: Dot Accuracy |
|
- type: manhattan_accuracy |
|
value: 0.5674 |
|
name: Manhattan Accuracy |
|
- type: euclidean_accuracy |
|
value: 0.5696 |
|
name: Euclidean Accuracy |
|
- type: max_accuracy |
|
value: 0.5696 |
|
name: Max Accuracy |
|
--- |
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|
|
# Fine-tuned snowflake actic xs based on MS-Marco triplets |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) on the sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 dataset. 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. |
|
|
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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:** [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) <!-- at revision 236cea8bda4680896324c8058c67e97c135eeb95 --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
|
## Usage |
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|
|
### Direct Usage (Sentence Transformers) |
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|
|
First install the Sentence Transformers library: |
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|
|
```bash |
|
pip install -U sentence-transformers |
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``` |
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|
|
Then you can load this model and run inference. |
|
```python |
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from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
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model = SentenceTransformer("krumeto/snowflake-arctic-embed-xs-ms-marco-triplet") |
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# Run inference |
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sentences = [ |
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'Represent this sentence for searching relevant passages: how many pitchers used per game', |
|
'In modern day baseball, teams generally have five starting pitchers, and they take it in turn to start a game every fifth day (hence the phrase rotation). Sometimes, if the schedule pans out, a team can get away with a four man rotation, and in the distant past some teams managed a three man rotation.', |
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'Accroding to the statistics in the Baseball Reference page showing 2014 Major League Baseball Pitching Pitches, 745 pitchers threw 704,983 pitches in 2430 games for an average of 290 pitches per game.', |
|
] |
|
embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
|
|
|
# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
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# [3, 3] |
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``` |
|
|
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Triplet |
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* Dataset: `xs-msmarco-triplet` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.571 | |
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| dot_accuracy | 0.4286 | |
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| manhattan_accuracy | 0.5728 | |
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| euclidean_accuracy | 0.571 | |
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| **max_accuracy** | **0.5728** | |
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|
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#### Triplet |
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* Dataset: `xs-msmarco-triplet-train` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.5696 | |
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| dot_accuracy | 0.43 | |
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| manhattan_accuracy | 0.5674 | |
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| euclidean_accuracy | 0.5696 | |
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| **max_accuracy** | **0.5696** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
|
### Training Dataset |
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|
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#### sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 |
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|
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* Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 |
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* Size: 100,000 training samples |
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* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | query | positive | negative | |
|
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | string | |
|
| details | <ul><li>min: 12 tokens</li><li>mean: 17.05 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 78.68 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 73.73 tokens</li><li>max: 205 tokens</li></ul> | |
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* Samples: |
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| query | positive | negative | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Represent this sentence for searching relevant passages: what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Liberal Arts Defined. The liberal arts are a set of academic disciplines that include the sciences and the humanities. When you study a liberal arts curriculum, you don't have to have one specific career goal, although you might. Instead, you'll assemble a broad foundation of knowledge that can be used in a wide spectrum of careers.</code> | |
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| <code>Represent this sentence for searching relevant passages: what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code> | <code>Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway.</code> | |
|
| <code>Represent this sentence for searching relevant passages: what is normal plat count</code> | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> | <code>In the context of blood work, PLT refers to the platelet count. Platelets are the cells that cause blood clotting and control bleeding. The normal range of platelets for adults is 3.5 to 10.5 billion cells per liter of blood, according to the Mayo Clinic. Continue Reading.</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/msmarco-msmarco-mini_lm-l-6-v3 |
|
|
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* Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 |
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* Size: 5,000 evaluation samples |
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* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | query | positive | negative | |
|
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | string | |
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| details | <ul><li>min: 12 tokens</li><li>mean: 17.09 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 80.07 tokens</li><li>max: 250 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 76.73 tokens</li><li>max: 341 tokens</li></ul> | |
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* Samples: |
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| query | positive | negative | |
|
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Represent this sentence for searching relevant passages: what is the name of the material harder than diamonds</code> | <code>Nano-material is harder than diamonds By Will Knight A material that is harder than diamond has been created in the lab, by packing together tiny ânanorodsâ of carbon. The new material, known as aggregated carbon nanorods (ACNR), was created by compressing and heating super-strong carbon molecules called buckyballs or carbon-60.</code> | <code>What material is stronger than diamond? Diamonds are famous for their hardness, but not particularly for strength,since they are easily cleaved to create the facets that make the different cuts of diamonds for setting in a ring or necklace. Many materials are stronger than diamonds, only a couple of synthesized materials are harder than diamonds.</code> | |
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| <code>Represent this sentence for searching relevant passages: is pink impression a perennial tulip?</code> | <code>Tulip Pink Impression. close video. VIDEO. Tulip Pink Impression. The rich pink blooms of this hybrid are bound to make an impression, whether used in the landscape or as a cut flower. Robust stems and giant blooms characterise the range, and this hybrid is no exception. âPink Impressionâ will continue to impress thanks to the perennial potential of this range.</code> | <code>Tulip Pink Impression. The lustrous petals are a deep, rich rose at the center, shading to a delicate pale pink at the edge, while doing amazing things in between that include shades of both melon and sunset. Tall, strong, long-lasting and reliable, like most Darwin hybrids. An absolutely first-class Tulip.</code> | |
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| <code>Represent this sentence for searching relevant passages: define: colonization</code> | <code>Colonization. the settlement and economic development of the uninhabited borderlands of a country (internal colonization) or the establishment of settlements (engaging primarily in agricultural activity) beyond the frontiers of a country (external colonization).</code> | <code>Colonization is a process by which a central system of power dominates the surrounding land and its components. The term is derived from the Latin word colere, which means to inhabit. Also, colonization refers strictly to migration, for example, to settler colonies in America or Australia, trading posts, and plantations, while colonialism deals with this, along with ruling the existing indigenous peoples of styled new territories. Colonization was linked to the spread of tens of millions fro</code> | |
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* 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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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
|
|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 1 |
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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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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
|
- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_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`: 1.0 |
|
- `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`: False |
|
- `fp16`: True |
|
- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | xs-msmarco-triplet-train_max_accuracy | xs-msmarco-triplet_max_accuracy | |
|
|:-----:|:----:|:-------------:|:------:|:-------------------------------------:|:-------------------------------:| |
|
| 0 | 0 | - | - | - | 0.3648 | |
|
| 0.016 | 100 | 1.0213 | 1.0078 | - | 0.3672 | |
|
| 0.032 | 200 | 0.9785 | 0.9630 | - | 0.3764 | |
|
| 0.048 | 300 | 0.9591 | 0.9190 | - | 0.4014 | |
|
| 0.064 | 400 | 0.9088 | 0.8906 | - | 0.4392 | |
|
| 0.08 | 500 | 0.9215 | 0.8707 | - | 0.4598 | |
|
| 0.096 | 600 | 0.8692 | 0.8681 | - | 0.4874 | |
|
| 0.112 | 700 | 0.8806 | 0.8538 | - | 0.4964 | |
|
| 0.128 | 800 | 0.8801 | 0.8477 | - | 0.5106 | |
|
| 0.144 | 900 | 0.8692 | 0.8414 | - | 0.5228 | |
|
| 0.16 | 1000 | 0.8624 | 0.8391 | - | 0.5194 | |
|
| 0.176 | 1100 | 0.8737 | 0.8397 | - | 0.5264 | |
|
| 0.192 | 1200 | 0.8505 | 0.8344 | - | 0.5214 | |
|
| 0.208 | 1300 | 0.8818 | 0.8358 | - | 0.5164 | |
|
| 0.224 | 1400 | 0.8464 | 0.8269 | - | 0.5326 | |
|
| 0.24 | 1500 | 0.8623 | 0.8291 | - | 0.5232 | |
|
| 0.256 | 1600 | 0.8203 | 0.8407 | - | 0.5328 | |
|
| 0.272 | 1700 | 0.8566 | 0.8257 | - | 0.5302 | |
|
| 0.288 | 1800 | 0.8386 | 0.8198 | - | 0.5364 | |
|
| 0.304 | 1900 | 0.8587 | 0.8172 | - | 0.5388 | |
|
| 0.32 | 2000 | 0.8472 | 0.8233 | - | 0.5568 | |
|
| 0.336 | 2100 | 0.8466 | 0.8188 | - | 0.5468 | |
|
| 0.352 | 2200 | 0.8273 | 0.8190 | - | 0.5386 | |
|
| 0.368 | 2300 | 0.8356 | 0.8183 | - | 0.5396 | |
|
| 0.384 | 2400 | 0.8294 | 0.8156 | - | 0.5538 | |
|
| 0.4 | 2500 | 0.8274 | 0.8168 | - | 0.5448 | |
|
| 0.416 | 2600 | 0.8392 | 0.8093 | - | 0.5422 | |
|
| 0.432 | 2700 | 0.8541 | 0.8087 | - | 0.5426 | |
|
| 0.448 | 2800 | 0.8218 | 0.8086 | - | 0.5474 | |
|
| 0.464 | 2900 | 0.8446 | 0.8062 | - | 0.554 | |
|
| 0.48 | 3000 | 0.8405 | 0.8076 | - | 0.548 | |
|
| 0.496 | 3100 | 0.8447 | 0.8087 | - | 0.553 | |
|
| 0.512 | 3200 | 0.8453 | 0.8073 | - | 0.5536 | |
|
| 0.528 | 3300 | 0.8371 | 0.8089 | - | 0.5504 | |
|
| 0.544 | 3400 | 0.8548 | 0.8005 | - | 0.5516 | |
|
| 0.56 | 3500 | 0.8162 | 0.8026 | - | 0.5572 | |
|
| 0.576 | 3600 | 0.8577 | 0.7994 | - | 0.5558 | |
|
| 0.592 | 3700 | 0.8289 | 0.7990 | - | 0.5526 | |
|
| 0.608 | 3800 | 0.824 | 0.7967 | - | 0.562 | |
|
| 0.624 | 3900 | 0.833 | 0.7959 | - | 0.5608 | |
|
| 0.64 | 4000 | 0.8362 | 0.7958 | - | 0.5554 | |
|
| 0.656 | 4100 | 0.8057 | 0.7966 | - | 0.5578 | |
|
| 0.672 | 4200 | 0.8001 | 0.7943 | - | 0.5646 | |
|
| 0.688 | 4300 | 0.8215 | 0.7937 | - | 0.5602 | |
|
| 0.704 | 4400 | 0.8257 | 0.7933 | - | 0.5614 | |
|
| 0.72 | 4500 | 0.8173 | 0.7942 | - | 0.5648 | |
|
| 0.736 | 4600 | 0.8002 | 0.7922 | - | 0.5698 | |
|
| 0.752 | 4700 | 0.8445 | 0.7899 | - | 0.5626 | |
|
| 0.768 | 4800 | 0.825 | 0.7897 | - | 0.5592 | |
|
| 0.784 | 4900 | 0.8151 | 0.7870 | - | 0.5696 | |
|
| 0.8 | 5000 | 0.8223 | 0.7895 | - | 0.5676 | |
|
| 0.816 | 5100 | 0.8235 | 0.7877 | - | 0.5656 | |
|
| 0.832 | 5200 | 0.8355 | 0.7866 | - | 0.5688 | |
|
| 0.848 | 5300 | 0.8218 | 0.7864 | - | 0.5672 | |
|
| 0.864 | 5400 | 0.8384 | 0.7866 | - | 0.5652 | |
|
| 0.88 | 5500 | 0.7988 | 0.7860 | - | 0.569 | |
|
| 0.896 | 5600 | 0.8117 | 0.7867 | - | 0.5684 | |
|
| 0.912 | 5700 | 0.8113 | 0.7861 | - | 0.5734 | |
|
| 0.928 | 5800 | 0.8129 | 0.7860 | - | 0.5698 | |
|
| 0.944 | 5900 | 0.799 | 0.7863 | - | 0.5688 | |
|
| 0.96 | 6000 | 0.8269 | 0.7858 | - | 0.5708 | |
|
| 0.976 | 6100 | 0.8066 | 0.7857 | - | 0.572 | |
|
| 0.992 | 6200 | 0.8302 | 0.7856 | - | 0.5728 | |
|
| 1.0 | 6250 | - | - | 0.5696 | - | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
|
- 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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