Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,441 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:78879
+- loss:CosineSimilarityLoss
+base_model: intfloat/multilingual-e5-base
+widget:
+- source_sentence: Somatotropin Ab
+  sentences:
+  - Desethylamiodarone
+  - Glucose^7H post XXX challenge
+  - Somatotropin Ab
+- source_sentence: Erythrocytes.fetal/1000 erythrocytes
+  sentences:
+  - levoFLOXacin
+  - Pathologist interpretation
+  - Pepsinogen I
+- source_sentence: Aggregazione piastrinica.arachidonato indotta
+  sentences:
+  - Epidermal growth factor
+  - Bilirubin.glucuronidated/Bilirubin.total
+  - Platelet aggregation.arachidonate induced
+- source_sentence: Parathormoon.intact^5 min na uitsnijding  in serum of plasma
+  sentences:
+  - Fatty acids.very long chain
+  - Estradiol^4th specimen post XXX challenge
+  - Parathyrin.intact^5M post excision
+- source_sentence: Karboksühemoglobiin/hemoglobiin.üld
+  sentences:
+  - Ammonia
+  - Carboxyhemoglobin/Hemoglobin.total
+  - Procainamide+N-acetylprocainamide
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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: XLMRobertaModel 
+  (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})
+  (2): Normalize()
+)
+```
+
+## 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("iddqd21/fine-tuned-e5-semantic-similarity")
+# Run inference
+sentences = [
+    'Karboksühemoglobiin/hemoglobiin.üld',
+    'Carboxyhemoglobin/Hemoglobin.total',
+    'Procainamide+N-acetylprocainamide',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+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.*
+-->
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+
+* Size: 78,879 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.64 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.26 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                        | sentence_1                                       | label            |
+  |:--------------------------------------------------|:-------------------------------------------------|:-----------------|
+  | <code>Rakud.CD3+HLA-DR+/100 raku kohta</code>     | <code>Cells.CD3+HLA-DR+/100 cells</code>         | <code>1.0</code> |
+  | <code>Zellen.FMC7/100 Zellen</code>               | <code>Cells.FMC7/100 cells</code>                | <code>1.0</code> |
+  | <code>Apolipoprotéine AI/apolipoprotéine B</code> | <code>Apolipoprotein A-I/Apolipoprotein B</code> | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `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
+- `torch_empty_cache_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
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: 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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.1014 | 500   | 0.0633        |
+| 0.2028 | 1000  | 0.0332        |
+| 0.3043 | 1500  | 0.0296        |
+| 0.4057 | 2000  | 0.0266        |
+| 0.5071 | 2500  | 0.024         |
+| 0.6085 | 3000  | 0.0239        |
+| 0.7099 | 3500  | 0.0216        |
+| 0.8114 | 4000  | 0.0205        |
+| 0.9128 | 4500  | 0.0187        |
+| 1.0142 | 5000  | 0.0185        |
+| 1.1156 | 5500  | 0.0149        |
+| 1.2170 | 6000  | 0.015         |
+| 1.3185 | 6500  | 0.0142        |
+| 1.4199 | 7000  | 0.0152        |
+| 1.5213 | 7500  | 0.0138        |
+| 1.6227 | 8000  | 0.0131        |
+| 1.7241 | 8500  | 0.014         |
+| 1.8256 | 9000  | 0.0133        |
+| 1.9270 | 9500  | 0.0125        |
+| 2.0284 | 10000 | 0.0128        |
+| 2.1298 | 10500 | 0.0093        |
+| 2.2312 | 11000 | 0.0091        |
+| 2.3327 | 11500 | 0.0097        |
+| 2.4341 | 12000 | 0.0096        |
+| 2.5355 | 12500 | 0.0097        |
+| 2.6369 | 13000 | 0.0093        |
+| 2.7383 | 13500 | 0.0099        |
+| 2.8398 | 14000 | 0.0104        |
+| 2.9412 | 14500 | 0.009         |
+| 3.0426 | 15000 | 0.0084        |
+| 3.1440 | 15500 | 0.0065        |
+| 3.2454 | 16000 | 0.0062        |
+| 3.3469 | 16500 | 0.0062        |
+| 3.4483 | 17000 | 0.0068        |
+| 3.5497 | 17500 | 0.0076        |
+| 3.6511 | 18000 | 0.0078        |
+| 3.7525 | 18500 | 0.0068        |
+| 3.8540 | 19000 | 0.008         |
+| 3.9554 | 19500 | 0.0076        |
+| 4.0568 | 20000 | 0.0057        |
+| 4.1582 | 20500 | 0.0054        |
+| 4.2596 | 21000 | 0.0052        |
+| 4.3611 | 21500 | 0.0052        |
+| 4.4625 | 22000 | 0.0056        |
+| 4.5639 | 22500 | 0.0055        |
+| 4.6653 | 23000 | 0.0057        |
+| 4.7667 | 23500 | 0.006         |
+| 4.8682 | 24000 | 0.0054        |
+| 4.9696 | 24500 | 0.0052        |
+| 5.0710 | 25000 | 0.0045        |
+| 5.1724 | 25500 | 0.0039        |
+| 5.2738 | 26000 | 0.0043        |
+| 5.3753 | 26500 | 0.004         |
+| 5.4767 | 27000 | 0.0044        |
+| 5.5781 | 27500 | 0.0045        |
+| 5.6795 | 28000 | 0.0039        |
+| 5.7809 | 28500 | 0.0043        |
+| 5.8824 | 29000 | 0.0047        |
+| 5.9838 | 29500 | 0.0049        |
+| 6.0852 | 30000 | 0.003         |
+| 6.1866 | 30500 | 0.0034        |
+| 6.2880 | 31000 | 0.003         |
+| 6.3895 | 31500 | 0.0031        |
+| 6.4909 | 32000 | 0.0033        |
+| 6.5923 | 32500 | 0.0035        |
+| 6.6937 | 33000 | 0.0037        |
+| 6.7951 | 33500 | 0.0039        |
+| 6.8966 | 34000 | 0.004         |
+| 6.9980 | 34500 | 0.003         |
+| 7.0994 | 35000 | 0.0024        |
+| 7.2008 | 35500 | 0.0026        |
+| 7.3022 | 36000 | 0.0029        |
+| 7.4037 | 36500 | 0.0029        |
+| 7.5051 | 37000 | 0.0025        |
+| 7.6065 | 37500 | 0.0026        |
+| 7.7079 | 38000 | 0.0032        |
+| 7.8093 | 38500 | 0.0032        |
+| 7.9108 | 39000 | 0.0029        |
+| 8.0122 | 39500 | 0.0028        |
+| 8.1136 | 40000 | 0.0024        |
+| 8.2150 | 40500 | 0.0021        |
+| 8.3164 | 41000 | 0.0022        |
+| 8.4178 | 41500 | 0.0022        |
+| 8.5193 | 42000 | 0.0024        |
+| 8.6207 | 42500 | 0.0025        |
+| 8.7221 | 43000 | 0.0023        |
+| 8.8235 | 43500 | 0.0021        |
+| 8.9249 | 44000 | 0.0026        |
+| 9.0264 | 44500 | 0.0025        |
+| 9.1278 | 45000 | 0.0021        |
+| 9.2292 | 45500 | 0.0017        |
+| 9.3306 | 46000 | 0.0022        |
+| 9.4320 | 46500 | 0.002         |
+| 9.5335 | 47000 | 0.0021        |
+| 9.6349 | 47500 | 0.0019        |
+| 9.7363 | 48000 | 0.0021        |
+| 9.8377 | 48500 | 0.002         |
+| 9.9391 | 49000 | 0.0021        |
+
+
+### Framework Versions
+- Python: 3.9.20
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+rocm6.2
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+rocm6.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:513a4f5efd9d804a97486757cec4b06fd09e02e4e40030a26f0b5056393ed564
+size 1112197096

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}