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README.md
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name: Spearman Max
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aari1995/gbert-large-nli_mix](https://huggingface.co/aari1995/gbert-large-nli_mix) on the [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) dataset. It maps sentences & paragraphs to a 1024-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 Description
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- **Model Type:** Sentence Transformer
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- **Base model:**
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- **Maximum Sequence Length:** 8192 tokens
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- **Output Dimensionality:** 1024 tokens
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- **Languages:** de
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<!-- - **License:** Unknown -->
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### Model Sources
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("aari1995/
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# Run inference
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sentences = [
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'Eine Flagge weht.',
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## Training Details
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### Training Dataset
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#### PhilipMay/stsb_multi_mt
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* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
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* Size: 22,996 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | score |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 6 tokens</li><li>mean: 18.13 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence1 | sentence2 | score |
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|:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------|
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| <code>schütze wegen mordes an schwarzem us-jugendlichen angeklagt</code> | <code>gedanken zu den rassenbeziehungen unter einem schwarzen präsidenten</code> | <code>0.1599999964237213</code> |
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| <code>fußballspieler kicken einen fußball in das tor.</code> | <code>Ein Fußballspieler schießt ein Tor.</code> | <code>0.7599999904632568</code> |
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| <code>obama lockert abschiebungsregeln für junge einwanderer</code> | <code>usa lockert abschiebebestimmungen für jugendliche: napolitano</code> | <code>0.800000011920929</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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"n_dims_per_step": -1
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}
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```
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#### PhilipMay/stsb_multi_mt
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* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
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* Size: 1,500 evaluation samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | score |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 5 tokens</li><li>mean: 16.54 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.53 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence1 | sentence2 | score |
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|:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
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| <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>1.0</code> |
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| <code>Ein kleines Kind reitet auf einem Pferd.</code> | <code>Ein Kind reitet auf einem Pferd.</code> | <code>0.949999988079071</code> |
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| <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</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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"loss": "ContrastiveLoss",
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"matryoshka_dims": [
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1024,
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768,
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512,
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256,
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128,
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64
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],
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"matryoshka_weights": [
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1,
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1,
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1,
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1,
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1,
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1
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],
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"n_dims_per_step": -1
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `learning_rate`: 5e-06
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- `num_train_epochs`: 4
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- `warmup_ratio`: 0.1
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 8
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- `per_device_eval_batch_size`: 8
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `learning_rate`: 5e-06
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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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`: 4
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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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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: False
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `batch_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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<details><summary>Click to expand</summary>
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| Epoch | Step | Training Loss | loss | sts-dev-1024_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-1024_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
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|:------:|:-----:|:-------------:|:------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
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| 0.0348 | 100 | 0.2334 | 0.2530 | 0.8329 | 0.8219 | 0.8274 | 0.8292 | 0.8148 | 0.8317 | - | - | - | - | - | - |
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| 0.0696 | 200 | 0.1959 | 0.1921 | 0.8285 | 0.8183 | 0.8234 | 0.8250 | 0.8121 | 0.8275 | - | - | - | - | - | - |
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| 0.1043 | 300 | 0.1468 | 0.1592 | 0.8346 | 0.8267 | 0.8305 | 0.8319 | 0.8227 | 0.8334 | - | - | - | - | - | - |
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| 0.1391 | 400 | 0.1346 | 0.1511 | 0.8513 | 0.8451 | 0.8486 | 0.8497 | 0.8418 | 0.8505 | - | - | - | - | - | - |
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| 0.1739 | 500 | 0.1333 | 0.1480 | 0.8590 | 0.8526 | 0.8563 | 0.8576 | 0.8502 | 0.8583 | - | - | - | - | - | - |
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| 0.2087 | 600 | 0.1328 | 0.1478 | 0.8626 | 0.8557 | 0.8595 | 0.8612 | 0.8530 | 0.8620 | - | - | - | - | - | - |
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| 0.2435 | 700 | 0.1345 | 0.1451 | 0.8631 | 0.8563 | 0.8599 | 0.8618 | 0.8548 | 0.8626 | - | - | - | - | - | - |
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| 0.2783 | 800 | 0.1282 | 0.1423 | 0.8705 | 0.8625 | 0.8671 | 0.8692 | 0.8601 | 0.8698 | - | - | - | - | - | - |
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| 0.3130 | 900 | 0.1317 | 0.1416 | 0.8724 | 0.8639 | 0.8690 | 0.8714 | 0.8619 | 0.8716 | - | - | - | - | - | - |
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| 0.3478 | 1000 | 0.1295 | 0.1422 | 0.8641 | 0.8577 | 0.8617 | 0.8637 | 0.8556 | 0.8639 | - | - | - | - | - | - |
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| 0.3826 | 1100 | 0.1267 | 0.1427 | 0.8675 | 0.8603 | 0.8644 | 0.8666 | 0.8581 | 0.8671 | - | - | - | - | - | - |
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| 0.4174 | 1200 | 0.127 | 0.1417 | 0.8674 | 0.8589 | 0.8635 | 0.8664 | 0.8570 | 0.8671 | - | - | - | - | - | - |
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| 0.4522 | 1300 | 0.1292 | 0.1419 | 0.8756 | 0.8663 | 0.8711 | 0.8739 | 0.8641 | 0.8748 | - | - | - | - | - | - |
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| 0.4870 | 1400 | 0.1281 | 0.1411 | 0.8726 | 0.8646 | 0.8686 | 0.8713 | 0.8616 | 0.8721 | - | - | - | - | - | - |
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| 0.5217 | 1500 | 0.1292 | 0.1407 | 0.8738 | 0.8654 | 0.8698 | 0.8727 | 0.8617 | 0.8739 | - | - | - | - | - | - |
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| 0.5565 | 1600 | 0.1251 | 0.1419 | 0.8732 | 0.8643 | 0.8686 | 0.8720 | 0.8605 | 0.8731 | - | - | - | - | - | - |
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| 0.5913 | 1700 | 0.1288 | 0.1412 | 0.8782 | 0.8682 | 0.8731 | 0.8769 | 0.8652 | 0.8779 | - | - | - | - | - | - |
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| 0.6261 | 1800 | 0.1306 | 0.1405 | 0.8755 | 0.8664 | 0.8710 | 0.8744 | 0.8632 | 0.8751 | - | - | - | - | - | - |
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| 0.6609 | 1900 | 0.1289 | 0.1410 | 0.8739 | 0.8647 | 0.8691 | 0.8727 | 0.8614 | 0.8736 | - | - | - | - | - | - |
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| 0.6957 | 2000 | 0.1287 | 0.1403 | 0.8773 | 0.8669 | 0.8719 | 0.8758 | 0.8637 | 0.8769 | - | - | - | - | - | - |
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| 0.7304 | 2100 | 0.126 | 0.1402 | 0.8773 | 0.8675 | 0.8722 | 0.8758 | 0.8635 | 0.8772 | - | - | - | - | - | - |
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| 0.7652 | 2200 | 0.1274 | 0.1401 | 0.8799 | 0.8693 | 0.8743 | 0.8784 | 0.8652 | 0.8797 | - | - | - | - | - | - |
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| 0.8 | 2300 | 0.1234 | 0.1399 | 0.8777 | 0.8686 | 0.8729 | 0.8767 | 0.8650 | 0.8778 | - | - | - | - | - | - |
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| 0.8348 | 2400 | 0.128 | 0.1401 | 0.8769 | 0.8660 | 0.8712 | 0.8759 | 0.8621 | 0.8768 | - | - | - | - | - | - |
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| 0.8696 | 2500 | 0.1269 | 0.1403 | 0.8756 | 0.8648 | 0.8698 | 0.8742 | 0.8605 | 0.8750 | - | - | - | - | - | - |
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| 0.9043 | 2600 | 0.1243 | 0.1401 | 0.8762 | 0.8665 | 0.8711 | 0.8751 | 0.8622 | 0.8760 | - | - | - | - | - | - |
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| 0.9391 | 2700 | 0.1277 | 0.1406 | 0.8742 | 0.8649 | 0.8693 | 0.8725 | 0.8613 | 0.8738 | - | - | - | - | - | - |
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| 0.9739 | 2800 | 0.1287 | 0.1394 | 0.8789 | 0.8689 | 0.8738 | 0.8773 | 0.8648 | 0.8785 | - | - | - | - | - | - |
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| 1.0087 | 2900 | 0.1274 | 0.1397 | 0.8784 | 0.8682 | 0.8731 | 0.8769 | 0.8632 | 0.8782 | - | - | - | - | - | - |
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| 729 |
-
| 1.0435 | 3000 | 0.129 | 0.1401 | 0.8800 | 0.8693 | 0.8743 | 0.8782 | 0.8653 | 0.8795 | - | - | - | - | - | - |
|
| 730 |
-
| 1.0783 | 3100 | 0.121 | 0.1408 | 0.8785 | 0.8682 | 0.8731 | 0.8769 | 0.8638 | 0.8782 | - | - | - | - | - | - |
|
| 731 |
-
| 1.1130 | 3200 | 0.1249 | 0.1399 | 0.8773 | 0.8668 | 0.8722 | 0.8759 | 0.8625 | 0.8771 | - | - | - | - | - | - |
|
| 732 |
-
| 1.1478 | 3300 | 0.1252 | 0.1404 | 0.8740 | 0.8643 | 0.8688 | 0.8724 | 0.8593 | 0.8737 | - | - | - | - | - | - |
|
| 733 |
-
| 1.1826 | 3400 | 0.126 | 0.1398 | 0.8761 | 0.8657 | 0.8707 | 0.8745 | 0.8610 | 0.8758 | - | - | - | - | - | - |
|
| 734 |
-
| 1.2174 | 3500 | 0.1279 | 0.1400 | 0.8760 | 0.8661 | 0.8708 | 0.8745 | 0.8617 | 0.8759 | - | - | - | - | - | - |
|
| 735 |
-
| 1.2522 | 3600 | 0.1264 | 0.1399 | 0.8786 | 0.8684 | 0.8734 | 0.8768 | 0.8633 | 0.8783 | - | - | - | - | - | - |
|
| 736 |
-
| 1.2870 | 3700 | 0.126 | 0.1395 | 0.8789 | 0.8690 | 0.8734 | 0.8773 | 0.8643 | 0.8786 | - | - | - | - | - | - |
|
| 737 |
-
| 1.3217 | 3800 | 0.1234 | 0.1399 | 0.8777 | 0.8669 | 0.8723 | 0.8760 | 0.8625 | 0.8775 | - | - | - | - | - | - |
|
| 738 |
-
| 1.3565 | 3900 | 0.1269 | 0.1397 | 0.8777 | 0.8671 | 0.8725 | 0.8760 | 0.8630 | 0.8773 | - | - | - | - | - | - |
|
| 739 |
-
| 1.3913 | 4000 | 0.1223 | 0.1393 | 0.8806 | 0.8694 | 0.8751 | 0.8789 | 0.8654 | 0.8802 | - | - | - | - | - | - |
|
| 740 |
-
| 1.4261 | 4100 | 0.1227 | 0.1399 | 0.8775 | 0.8671 | 0.8728 | 0.8764 | 0.8622 | 0.8774 | - | - | - | - | - | - |
|
| 741 |
-
| 1.4609 | 4200 | 0.1263 | 0.1402 | 0.8771 | 0.8669 | 0.8724 | 0.8756 | 0.8619 | 0.8769 | - | - | - | - | - | - |
|
| 742 |
-
| 1.4957 | 4300 | 0.1263 | 0.1400 | 0.8781 | 0.8674 | 0.8730 | 0.8766 | 0.8627 | 0.8778 | - | - | - | - | - | - |
|
| 743 |
-
| 1.5304 | 4400 | 0.1302 | 0.1396 | 0.8778 | 0.8675 | 0.8728 | 0.8761 | 0.8628 | 0.8775 | - | - | - | - | - | - |
|
| 744 |
-
| 1.5652 | 4500 | 0.1274 | 0.1393 | 0.8789 | 0.8685 | 0.8736 | 0.8770 | 0.8637 | 0.8784 | - | - | - | - | - | - |
|
| 745 |
-
| 1.6 | 4600 | 0.1273 | 0.1394 | 0.8794 | 0.8683 | 0.8737 | 0.8773 | 0.8637 | 0.8789 | - | - | - | - | - | - |
|
| 746 |
-
| 1.6348 | 4700 | 0.1297 | 0.1391 | 0.8822 | 0.8712 | 0.8764 | 0.8800 | 0.8666 | 0.8817 | - | - | - | - | - | - |
|
| 747 |
-
| 1.6696 | 4800 | 0.1249 | 0.1392 | 0.8804 | 0.8694 | 0.8748 | 0.8785 | 0.8643 | 0.8802 | - | - | - | - | - | - |
|
| 748 |
-
| 1.7043 | 4900 | 0.1286 | 0.1390 | 0.8803 | 0.8693 | 0.8746 | 0.8784 | 0.8643 | 0.8800 | - | - | - | - | - | - |
|
| 749 |
-
| 1.7391 | 5000 | 0.1271 | 0.1392 | 0.8799 | 0.8697 | 0.8745 | 0.8780 | 0.8645 | 0.8795 | - | - | - | - | - | - |
|
| 750 |
-
| 1.7739 | 5100 | 0.1293 | 0.1391 | 0.8803 | 0.8702 | 0.8748 | 0.8790 | 0.8648 | 0.8803 | - | - | - | - | - | - |
|
| 751 |
-
| 1.8087 | 5200 | 0.1233 | 0.1391 | 0.8793 | 0.8692 | 0.8739 | 0.8777 | 0.8639 | 0.8791 | - | - | - | - | - | - |
|
| 752 |
-
| 1.8435 | 5300 | 0.1239 | 0.1394 | 0.8805 | 0.8705 | 0.8748 | 0.8788 | 0.8656 | 0.8802 | - | - | - | - | - | - |
|
| 753 |
-
| 1.8783 | 5400 | 0.124 | 0.1392 | 0.8795 | 0.8692 | 0.8742 | 0.8780 | 0.8640 | 0.8792 | - | - | - | - | - | - |
|
| 754 |
-
| 1.9130 | 5500 | 0.1245 | 0.1390 | 0.8797 | 0.8697 | 0.8744 | 0.8782 | 0.8645 | 0.8794 | - | - | - | - | - | - |
|
| 755 |
-
| 1.9478 | 5600 | 0.1257 | 0.1391 | 0.8794 | 0.8689 | 0.8741 | 0.8778 | 0.8637 | 0.8791 | - | - | - | - | - | - |
|
| 756 |
-
| 1.9826 | 5700 | 0.1231 | 0.1389 | 0.8807 | 0.8708 | 0.8756 | 0.8793 | 0.8663 | 0.8804 | - | - | - | - | - | - |
|
| 757 |
-
| 2.0174 | 5800 | 0.1216 | 0.1390 | 0.8781 | 0.8678 | 0.8733 | 0.8768 | 0.8630 | 0.8779 | - | - | - | - | - | - |
|
| 758 |
-
| 2.0522 | 5900 | 0.1252 | 0.1387 | 0.8795 | 0.8695 | 0.8745 | 0.8784 | 0.8639 | 0.8794 | - | - | - | - | - | - |
|
| 759 |
-
| 2.0870 | 6000 | 0.1242 | 0.1387 | 0.8799 | 0.8703 | 0.8749 | 0.8787 | 0.8652 | 0.8798 | - | - | - | - | - | - |
|
| 760 |
-
| 2.1217 | 6100 | 0.1231 | 0.1392 | 0.8796 | 0.8702 | 0.8748 | 0.8784 | 0.8653 | 0.8795 | - | - | - | - | - | - |
|
| 761 |
-
| 2.1565 | 6200 | 0.1217 | 0.1391 | 0.8797 | 0.8704 | 0.8746 | 0.8784 | 0.8655 | 0.8794 | - | - | - | - | - | - |
|
| 762 |
-
| 2.1913 | 6300 | 0.1259 | 0.1389 | 0.8803 | 0.8710 | 0.8756 | 0.8789 | 0.8664 | 0.8800 | - | - | - | - | - | - |
|
| 763 |
-
| 2.2261 | 6400 | 0.1262 | 0.1386 | 0.8813 | 0.8714 | 0.8762 | 0.8796 | 0.8667 | 0.8809 | - | - | - | - | - | - |
|
| 764 |
-
| 2.2609 | 6500 | 0.127 | 0.1392 | 0.8793 | 0.8701 | 0.8743 | 0.8778 | 0.8652 | 0.8792 | - | - | - | - | - | - |
|
| 765 |
-
| 2.2957 | 6600 | 0.1275 | 0.1391 | 0.8806 | 0.8710 | 0.8755 | 0.8788 | 0.8663 | 0.8803 | - | - | - | - | - | - |
|
| 766 |
-
| 2.3304 | 6700 | 0.1228 | 0.1394 | 0.8795 | 0.8693 | 0.8741 | 0.8774 | 0.8646 | 0.8791 | - | - | - | - | - | - |
|
| 767 |
-
| 2.3652 | 6800 | 0.1243 | 0.1390 | 0.8803 | 0.8700 | 0.8747 | 0.8783 | 0.8655 | 0.8797 | - | - | - | - | - | - |
|
| 768 |
-
| 2.4 | 6900 | 0.1292 | 0.1389 | 0.8795 | 0.8697 | 0.8743 | 0.8778 | 0.8650 | 0.8791 | - | - | - | - | - | - |
|
| 769 |
-
| 2.4348 | 7000 | 0.1238 | 0.1390 | 0.8799 | 0.8697 | 0.8744 | 0.8782 | 0.8648 | 0.8795 | - | - | - | - | - | - |
|
| 770 |
-
| 2.4696 | 7100 | 0.1246 | 0.1389 | 0.8800 | 0.8695 | 0.8743 | 0.8780 | 0.8649 | 0.8795 | - | - | - | - | - | - |
|
| 771 |
-
| 2.5043 | 7200 | 0.1265 | 0.1396 | 0.8802 | 0.8695 | 0.8743 | 0.8781 | 0.8647 | 0.8796 | - | - | - | - | - | - |
|
| 772 |
-
| 2.5391 | 7300 | 0.1229 | 0.1390 | 0.8813 | 0.8708 | 0.8753 | 0.8796 | 0.8665 | 0.8809 | - | - | - | - | - | - |
|
| 773 |
-
| 2.5739 | 7400 | 0.1244 | 0.1389 | 0.8808 | 0.8706 | 0.8749 | 0.8790 | 0.8665 | 0.8803 | - | - | - | - | - | - |
|
| 774 |
-
| 2.6087 | 7500 | 0.1223 | 0.1389 | 0.8813 | 0.8709 | 0.8753 | 0.8797 | 0.8662 | 0.8807 | - | - | - | - | - | - |
|
| 775 |
-
| 2.6435 | 7600 | 0.1268 | 0.1387 | 0.8810 | 0.8704 | 0.8752 | 0.8793 | 0.8659 | 0.8805 | - | - | - | - | - | - |
|
| 776 |
-
| 2.6783 | 7700 | 0.1218 | 0.1387 | 0.8817 | 0.8710 | 0.8755 | 0.8798 | 0.8665 | 0.8809 | - | - | - | - | - | - |
|
| 777 |
-
| 2.7130 | 7800 | 0.1225 | 0.1388 | 0.8804 | 0.8700 | 0.8745 | 0.8787 | 0.8653 | 0.8799 | - | - | - | - | - | - |
|
| 778 |
-
| 2.7478 | 7900 | 0.1263 | 0.1391 | 0.8807 | 0.8703 | 0.8745 | 0.8788 | 0.8654 | 0.8801 | - | - | - | - | - | - |
|
| 779 |
-
| 2.7826 | 8000 | 0.1261 | 0.1388 | 0.8804 | 0.8698 | 0.8743 | 0.8787 | 0.8652 | 0.8799 | - | - | - | - | - | - |
|
| 780 |
-
| 2.8174 | 8100 | 0.1267 | 0.1386 | 0.8814 | 0.8707 | 0.8750 | 0.8795 | 0.8658 | 0.8807 | - | - | - | - | - | - |
|
| 781 |
-
| 2.8522 | 8200 | 0.1236 | 0.1387 | 0.8809 | 0.8703 | 0.8747 | 0.8792 | 0.8659 | 0.8803 | - | - | - | - | - | - |
|
| 782 |
-
| 2.8870 | 8300 | 0.1222 | 0.1390 | 0.8802 | 0.8696 | 0.8741 | 0.8786 | 0.8649 | 0.8799 | - | - | - | - | - | - |
|
| 783 |
-
| 2.9217 | 8400 | 0.1236 | 0.1388 | 0.8807 | 0.8700 | 0.8747 | 0.8790 | 0.8653 | 0.8802 | - | - | - | - | - | - |
|
| 784 |
-
| 2.9565 | 8500 | 0.1233 | 0.1389 | 0.8808 | 0.8705 | 0.8752 | 0.8791 | 0.8659 | 0.8806 | - | - | - | - | - | - |
|
| 785 |
-
| 2.9913 | 8600 | 0.1262 | 0.1388 | 0.8808 | 0.8704 | 0.8750 | 0.8792 | 0.8658 | 0.8805 | - | - | - | - | - | - |
|
| 786 |
-
| 3.0261 | 8700 | 0.1277 | 0.1388 | 0.8795 | 0.8690 | 0.8737 | 0.8778 | 0.8640 | 0.8791 | - | - | - | - | - | - |
|
| 787 |
-
| 3.0609 | 8800 | 0.1243 | 0.1387 | 0.8809 | 0.8705 | 0.8751 | 0.8791 | 0.8656 | 0.8803 | - | - | - | - | - | - |
|
| 788 |
-
| 3.0957 | 8900 | 0.1206 | 0.1387 | 0.8813 | 0.8709 | 0.8754 | 0.8796 | 0.8661 | 0.8807 | - | - | - | - | - | - |
|
| 789 |
-
| 3.1304 | 9000 | 0.1217 | 0.1388 | 0.8815 | 0.8716 | 0.8758 | 0.8797 | 0.8670 | 0.8810 | - | - | - | - | - | - |
|
| 790 |
-
| 3.1652 | 9100 | 0.1236 | 0.1390 | 0.8803 | 0.8702 | 0.8744 | 0.8785 | 0.8653 | 0.8798 | - | - | - | - | - | - |
|
| 791 |
-
| 3.2 | 9200 | 0.1244 | 0.1389 | 0.8799 | 0.8697 | 0.8741 | 0.8783 | 0.8647 | 0.8795 | - | - | - | - | - | - |
|
| 792 |
-
| 3.2348 | 9300 | 0.1247 | 0.1388 | 0.8802 | 0.8698 | 0.8743 | 0.8785 | 0.8650 | 0.8798 | - | - | - | - | - | - |
|
| 793 |
-
| 3.2696 | 9400 | 0.1214 | 0.1388 | 0.8810 | 0.8710 | 0.8751 | 0.8793 | 0.8663 | 0.8806 | - | - | - | - | - | - |
|
| 794 |
-
| 3.3043 | 9500 | 0.121 | 0.1386 | 0.8808 | 0.8709 | 0.8749 | 0.8791 | 0.8662 | 0.8803 | - | - | - | - | - | - |
|
| 795 |
-
| 3.3391 | 9600 | 0.1205 | 0.1387 | 0.8804 | 0.8705 | 0.8746 | 0.8789 | 0.8655 | 0.8800 | - | - | - | - | - | - |
|
| 796 |
-
| 3.3739 | 9700 | 0.1203 | 0.1387 | 0.8807 | 0.8708 | 0.8750 | 0.8790 | 0.8661 | 0.8802 | - | - | - | - | - | - |
|
| 797 |
-
| 3.4087 | 9800 | 0.1239 | 0.1386 | 0.8811 | 0.8711 | 0.8752 | 0.8794 | 0.8663 | 0.8805 | - | - | - | - | - | - |
|
| 798 |
-
| 3.4435 | 9900 | 0.1197 | 0.1387 | 0.8808 | 0.8709 | 0.8750 | 0.8792 | 0.8662 | 0.8804 | - | - | - | - | - | - |
|
| 799 |
-
| 3.4783 | 10000 | 0.1252 | 0.1388 | 0.8805 | 0.8704 | 0.8746 | 0.8787 | 0.8657 | 0.8800 | - | - | - | - | - | - |
|
| 800 |
-
| 3.5130 | 10100 | 0.1229 | 0.1388 | 0.8803 | 0.8703 | 0.8745 | 0.8786 | 0.8654 | 0.8799 | - | - | - | - | - | - |
|
| 801 |
-
| 3.5478 | 10200 | 0.1258 | 0.1387 | 0.8805 | 0.8704 | 0.8747 | 0.8787 | 0.8653 | 0.8801 | - | - | - | - | - | - |
|
| 802 |
-
| 3.5826 | 10300 | 0.1232 | 0.1387 | 0.8806 | 0.8706 | 0.8750 | 0.8790 | 0.8656 | 0.8802 | - | - | - | - | - | - |
|
| 803 |
-
| 3.6174 | 10400 | 0.1286 | 0.1388 | 0.8807 | 0.8706 | 0.8749 | 0.8790 | 0.8656 | 0.8802 | - | - | - | - | - | - |
|
| 804 |
-
| 3.6522 | 10500 | 0.1248 | 0.1387 | 0.8806 | 0.8706 | 0.8748 | 0.8789 | 0.8653 | 0.8802 | - | - | - | - | - | - |
|
| 805 |
-
| 3.6870 | 10600 | 0.1277 | 0.1389 | 0.8800 | 0.8699 | 0.8742 | 0.8782 | 0.8647 | 0.8796 | - | - | - | - | - | - |
|
| 806 |
-
| 3.7217 | 10700 | 0.1219 | 0.1388 | 0.8799 | 0.8697 | 0.8740 | 0.8780 | 0.8645 | 0.8794 | - | - | - | - | - | - |
|
| 807 |
-
| 3.7565 | 10800 | 0.1269 | 0.1388 | 0.8803 | 0.8702 | 0.8745 | 0.8785 | 0.8649 | 0.8798 | - | - | - | - | - | - |
|
| 808 |
-
| 3.7913 | 10900 | 0.1289 | 0.1387 | 0.8805 | 0.8703 | 0.8746 | 0.8787 | 0.8651 | 0.8800 | - | - | - | - | - | - |
|
| 809 |
-
| 3.8261 | 11000 | 0.1234 | 0.1387 | 0.8806 | 0.8704 | 0.8749 | 0.8789 | 0.8653 | 0.8801 | - | - | - | - | - | - |
|
| 810 |
-
| 3.8609 | 11100 | 0.1229 | 0.1387 | 0.8806 | 0.8706 | 0.8749 | 0.8788 | 0.8654 | 0.8802 | - | - | - | - | - | - |
|
| 811 |
-
| 3.8957 | 11200 | 0.1266 | 0.1387 | 0.8806 | 0.8706 | 0.8749 | 0.8789 | 0.8655 | 0.8801 | - | - | - | - | - | - |
|
| 812 |
-
| 3.9304 | 11300 | 0.1253 | 0.1387 | 0.8804 | 0.8704 | 0.8747 | 0.8787 | 0.8653 | 0.8800 | - | - | - | - | - | - |
|
| 813 |
-
| 3.9652 | 11400 | 0.1279 | 0.1388 | 0.8804 | 0.8704 | 0.8747 | 0.8787 | 0.8653 | 0.8799 | - | - | - | - | - | - |
|
| 814 |
-
| 4.0 | 11500 | 0.1195 | 0.1388 | 0.8804 | 0.8704 | 0.8747 | 0.8786 | 0.8652 | 0.8799 | 0.8623 | 0.8546 | 0.8583 | 0.8624 | 0.8507 | 0.8632 |
|
| 815 |
-
|
| 816 |
-
</details>
|
| 817 |
-
|
| 818 |
-
### Framework Versions
|
| 819 |
-
- Python: 3.9.16
|
| 820 |
-
- Sentence Transformers: 3.0.0
|
| 821 |
-
- Transformers: 4.42.0.dev0
|
| 822 |
-
- PyTorch: 2.2.2+cu118
|
| 823 |
-
- Accelerate: 0.31.0
|
| 824 |
-
- Datasets: 2.19.1
|
| 825 |
-
- Tokenizers: 0.19.1
|
| 826 |
|
| 827 |
## Citation
|
| 828 |
|
|
|
|
| 276 |
name: Spearman Max
|
| 277 |
---
|
| 278 |
|
| 279 |
+
# German Semantic V3
|
| 280 |
+
|
| 281 |
+
The successor of German_Semantic_STS_V2 is here!
|
| 282 |
+
|
| 283 |
+
## Major updates:
|
| 284 |
+
|
| 285 |
+
- **Sequence length: 8192, (16 times more than V2 and other models) => thanks to the alibi implementation of Jina-Team!**
|
| 286 |
+
- **Matryoshka Embeddings: Your embeddings can be sized from 1024 down to 64**
|
| 287 |
+
- **License: Apache 2.0**
|
| 288 |
|
|
|
|
| 289 |
|
| 290 |
## Model Details
|
| 291 |
|
| 292 |
### Model Description
|
| 293 |
- **Model Type:** Sentence Transformer
|
| 294 |
+
- **Base model:** gbert-large (alibi applied)
|
| 295 |
- **Maximum Sequence Length:** 8192 tokens
|
| 296 |
- **Output Dimensionality:** 1024 tokens
|
| 297 |
- **Similarity Function:** Cosine Similarity
|
| 298 |
- **Training Dataset:**
|
| 299 |
+
- multiple German datasets
|
| 300 |
+
- **Languages:** de
|
| 301 |
<!-- - **License:** Unknown -->
|
| 302 |
|
| 303 |
### Model Sources
|
|
|
|
| 330 |
from sentence_transformers import SentenceTransformer
|
| 331 |
|
| 332 |
# Download from the 🤗 Hub
|
| 333 |
+
model = SentenceTransformer("aari1995/German_Semantic_V3", trust_remote_code=True)
|
| 334 |
# Run inference
|
| 335 |
sentences = [
|
| 336 |
'Eine Flagge weht.',
|
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|
| 492 |
|
| 493 |
## Training Details
|
| 494 |
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|
| 495 |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 496 |
```json
|
| 497 |
{
|
|
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|
| 515 |
"n_dims_per_step": -1
|
| 516 |
}
|
| 517 |
```
|
| 518 |
+
## License / Credits and Special thanks to:
|
| 519 |
|
| 520 |
+
- to [Jina AI](https://huggingface.co/jinaai) for the model architecture, especially their ALiBi implementation
|
| 521 |
+
- to [deepset](https://huggingface.co/deepset) for gbert-large, which is imho still the greatest German model
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| 522 |
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| 523 |
## Citation
|
| 524 |
|