Sentence Similarity
sentence-transformers
Safetensors
MLX
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:6661966
loss:MultipleNegativesRankingLoss
loss:CachedMultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:AnglELoss
loss:CoSENTLoss
loss:CosineSimilarityLoss
text-embeddings-inference
language: | |
- en | |
tags: | |
- sentence-transformers | |
- sentence-similarity | |
- feature-extraction | |
- generated_from_trainer | |
- dataset_size:6661966 | |
- loss:MultipleNegativesRankingLoss | |
- loss:CachedMultipleNegativesRankingLoss | |
- loss:SoftmaxLoss | |
- loss:AnglELoss | |
- loss:CoSENTLoss | |
- loss:CosineSimilarityLoss | |
- mlx | |
base_model: answerdotai/ModernBERT-base | |
widget: | |
- source_sentence: Daniel went to the kitchen. Sandra went back to the kitchen. Daniel | |
moved to the garden. Sandra grabbed the apple. Sandra went back to the office. | |
Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. | |
Sandra went back to the office. Mary went back to the office. Daniel moved to | |
the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra | |
put down the apple there. Mary went back to the bathroom. Daniel travelled to | |
the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. | |
Sandra journeyed to the bedroom. John travelled to the office. John went back | |
to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left | |
the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. | |
Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the | |
apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the | |
football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary | |
travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. | |
Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. | |
Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the | |
bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled | |
to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra | |
dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed | |
to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra | |
got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel | |
travelled to the garden. Sandra went back to the bathroom. Sandra discarded the | |
football. | |
sentences: | |
- In the adulthood stage, it can jump, walk, run | |
- The chocolate is bigger than the container. | |
- The football before the bathroom was in the garden. | |
- source_sentence: Almost everywhere the series converges then . | |
sentences: | |
- The series then converges almost everywhere . | |
- Scrivener dated the manuscript to the 12th century , C. R. Gregory to the 13th | |
century . Currently the manuscript is dated by the INTF to the 12th century . | |
- Both daughters died before he did , Tosca in 1976 and Janear in 1981 . | |
- source_sentence: how are you i'm doing good thank you you im not good having cough | |
and colg | |
sentences: | |
- 'This example tweet expresses the emotion: happiness' | |
- This example utterance is about cooking recipies. | |
- This example text from a US presidential speech is about macroeconomics | |
- source_sentence: A man is doing pull-ups | |
sentences: | |
- The man is doing exercises in a gym | |
- A black and white dog with a large branch is running in the field | |
- There is no man drawing | |
- source_sentence: A chef is preparing some food | |
sentences: | |
- The man is lifting weights | |
- A chef is preparing a meal | |
- A dog is in a sandy area with the sand that is being stirred up into the air and | |
several plants are in the background | |
datasets: | |
- tomaarsen/natural-questions-hard-negatives | |
- tomaarsen/gooaq-hard-negatives | |
- bclavie/msmarco-500k-triplets | |
- sentence-transformers/all-nli | |
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 | |
- sentence-transformers/gooaq | |
- sentence-transformers/natural-questions | |
- tasksource/merged-2l-nli | |
- tasksource/merged-3l-nli | |
- tasksource/zero-shot-label-nli | |
- MoritzLaurer/dataset_train_nli | |
- google-research-datasets/paws | |
- nyu-mll/glue | |
- mwong/fever-evidence-related | |
- tasksource/sts-companion | |
pipeline_tag: sentence-similarity | |
library_name: sentence-transformers | |
# mlx-community/tasksource-ModernBERT-base-embed-bf16 | |
The Model [mlx-community/tasksource-ModernBERT-base-embed-bf16](https://huggingface.co/mlx-community/tasksource-ModernBERT-base-embed-bf16) was converted to MLX format from [tasksource/ModernBERT-base-embed](https://huggingface.co/tasksource/ModernBERT-base-embed) using mlx-lm version **0.0.3**. | |
## Use with mlx | |
```bash | |
pip install mlx-embeddings | |
``` | |
```python | |
from mlx_embeddings import load, generate | |
import mlx.core as mx | |
model, tokenizer = load("mlx-community/tasksource-ModernBERT-base-embed-bf16") | |
# For text embeddings | |
output = generate(model, processor, texts=["I like grapes", "I like fruits"]) | |
embeddings = output.text_embeds # Normalized embeddings | |
# Compute dot product between normalized embeddings | |
similarity_matrix = mx.matmul(embeddings, embeddings.T) | |
print("Similarity matrix between texts:") | |
print(similarity_matrix) | |
``` | |