Add new SentenceTransformer model

0_MMContextEncoder/config.json

@@ -0,0 +1,16 @@
+{
+    "text_encoder_name": "NeuML/pubmedbert-base-embeddings",
+    "adapter_hidden_dim": 512,
+    "adapter_output_dim": 1024,
+    "freeze_text_encoder": true,
+    "unfreeze_last_n_layers": 0,
+    "registered_data_origin": "unregistered",
+    "registered_input_dim": null,
+    "output_token_embeddings": false,
+    "train_lookup": false,
+    "pooling_mode": "mean",
+    "joint_adapter_hidden_dim": null,
+    "_joint_adapter_was_trained": false,
+    "max_seq_length": 512,
+    "text_model_kwargs": {}
+}

0_MMContextEncoder/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63acb083c62bf243c3bbd39d1aba7320c64bbe3e4b14acac01d4b58bb0045f0d
+size 441647280

README.md

@@ -0,0 +1,504 @@
+---
+language:
+- code
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:81143
+- loss:MultipleNegativesRankingLoss
+base_model: NeuML/pubmedbert-base-embeddings
+widget:
+- source_sentence: sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_1563
+  sentences:
+  - This measurement was conducted with 10x 5' v1. Naive B cell from blood of a 26-year
+    old male, activated with CD3.
+  - sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_5036
+  - This measurement was conducted with 10x 5' v1. A 26-year-old male individual's
+    blood sample, containing naive thymus-derived CD4-positive, alpha-beta T cells,
+    with no activation or treatment, and in G1 phase.
+- source_sentence: sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_738
+  sentences:
+  - This measurement was conducted with 10x 3' v3. Blasts cells derived from the blood
+    of a 4-month old male.
+  - sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_1016
+  - This measurement was conducted with 10x 3' v3. This is a megakaryocyte-erythroid
+    progenitor cell (MEP-like) derived from a 1-month-old female patient with KMT2A-rearranged
+    (KMT2A-r) infant acute lymphoblastic leukemia (ALL). The cell exhibits increased
+    lineage plasticity, downregulated steroid response pathways, and belongs to a
+    hematopoietic stem and progenitor-like (HSPC-like) population that forms an immunosuppressive
+    signaling circuit with cytotoxic lymphocytes.
+- source_sentence: sample_idx:census_2872f4b0-b171-46e2-abc6-befcf6de6306_2050
+  sentences:
+  - sample_idx:census_2872f4b0-b171-46e2-abc6-befcf6de6306_1719
+  - This measurement was conducted with 10x 5' v2. Memory B cell derived from a 65-79
+    year-old male, taken from the mesenteric lymph node.
+  - This measurement was conducted with 10x 5' v2. IgA plasma cell sample taken from
+    the mesenteric lymph node of a 65-79 year-old female.
+- source_sentence: sample_idx:census_3f31f8ce-bbf6-4df8-8203-aa240ed03026_299
+  sentences:
+  - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old
+    male human cerebral cortex, specifically from the Cingulate gyrus, rostral (CgGr),
+    Ventral division of MFC - A24 region, with European self-reported ethnicity, analyzed
+    at the nucleus level.
+  - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old
+    male human cerebral cortex, specifically the rostral cingulate gyrus, ventral
+    division of MFC, A24, with European ethnicity.
+  - sample_idx:census_3f31f8ce-bbf6-4df8-8203-aa240ed03026_30
+- source_sentence: sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_14644
+  sentences:
+  - sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_16130
+  - This measurement was conducted with 10x 3' v3. Classical monocytes derived from
+    the blood of a female individual in her seventies.
+  - This measurement was conducted with 10x 5' v2. Sample is a CD8-positive, alpha-beta
+    memory T cell, specifically a cytotoxic T cell, from the lamina propria tissue
+    of an individual in her eighth decade of life.
+datasets:
+- jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on NeuML/pubmedbert-base-embeddings
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: cellxgene pseudo bulk 100k multiplets natural language annotation cell
+        sentence 1
+      type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1
+    metrics:
+    - type: cosine_accuracy
+      value: 0.5158140063285828
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on NeuML/pubmedbert-base-embeddings
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on the [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation)
+- **Language:** code
+<!-- - **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): MMContextEncoder(
+    (text_encoder): BertModel(
+      (embeddings): BertEmbeddings(
+        (word_embeddings): Embedding(30522, 768, padding_idx=0)
+        (position_embeddings): Embedding(512, 768)
+        (token_type_embeddings): Embedding(2, 768)
+        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (encoder): BertEncoder(
+        (layer): ModuleList(
+          (0-11): 12 x BertLayer(
+            (attention): BertAttention(
+              (self): BertSdpaSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (output): BertSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+            (intermediate): BertIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BertOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+        )
+      )
+      (pooler): BertPooler(
+        (dense): Linear(in_features=768, out_features=768, bias=True)
+        (activation): Tanh()
+      )
+    )
+    (text_adapter): AdapterModule(
+      (net): Sequential(
+        (0): Linear(in_features=768, out_features=512, bias=True)
+        (1): ReLU(inplace=True)
+        (2): Linear(in_features=512, out_features=1024, bias=True)
+        (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (pooling): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  )
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("jo-mengr/mmcontext-pubmedbert-100k")
+# Run inference
+sentences = [
+    'sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_14644',
+    "This measurement was conducted with 10x 5' v2. Sample is a CD8-positive, alpha-beta memory T cell, specifically a cytotoxic T cell, from the lamina propria tissue of an individual in her eighth decade of life.",
+    "This measurement was conducted with 10x 3' v3. Classical monocytes derived from the blood of a female individual in her seventies.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000, -0.0963, -0.2026],
+#         [-0.0963,  1.0000,  0.9080],
+#         [-0.2026,  0.9080,  1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Dataset: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.5158** |
+
+<!--
+## 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
+
+#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
+
+* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [9916878](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/9916878bbf20fb8f9d6a0be4c997236e027cabd4)
+* Size: 81,143 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                                         | positive                                                                                         | negative_1                                                                                        | negative_2                                                                                     |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | string                                                                                            | string                                                                                         |
+  | details | <ul><li>min: 56 characters</li><li>mean: 58.72 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 92 characters</li><li>mean: 216.13 characters</li><li>max: 900 characters</li></ul> | <ul><li>min: 101 characters</li><li>mean: 215.14 characters</li><li>max: 870 characters</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.75 characters</li><li>max: 60 characters</li></ul> |
+* Samples:
+  | anchor                                                                    | positive                                                                                                                                                                                                                                                     | negative_1                                                                                                                                                                                                                                                                                                                                                                                                             | negative_2                                                                |
+  |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|
+  | <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009</code> | <code>This measurement was conducted with 10x 3' v2. A proliferating lymphocyte cell sample, obtained from a 34-year-old female Asian individual, derived from peripheral blood mononuclear cells.</code>                                                    | <code>This measurement was conducted with 10x 3' v2. Sample is a 25-year-old female with European ethnicity, having CD8-positive, alpha-beta T cell type. This cell type exhibits elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells correlating with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells.</code> | <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_14165</code> |
+  | <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333</code>  | <code>This measurement was conducted with 10x 5' v1. Sample is a cell from the omentum tissue, specifically an effector memory CD4-positive, alpha-beta T cell, from a female in her sixth decade.</code>                                                    | <code>This measurement was conducted with 10x 5' v2. Conventional dendritic cell from the jejunal epithelium of a female in her eighth decade.</code>                                                                                                                                                                                                                                                                  | <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_2714</code>  |
+  | <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271</code>   | <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male, specifically from the thalamic complex, specifically the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG).</code> | <code>This measurement was conducted with 10x 3' v3. Neuron from the thalamic complex (thalamus, posterior nuclear complex of thalamus, medial geniculate nuclei) of a 42-year-old male, identified as a midbrain-derived inhibitory neuron.</code>                                                                                                                                                                    | <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_425</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
+
+#### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation
+
+* Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [9916878](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/9916878bbf20fb8f9d6a0be4c997236e027cabd4)
+* Size: 9,011 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                                         | positive                                                                                         | negative_1                                                                                        | negative_2                                                                                     |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | string                                                                                            | string                                                                                         |
+  | details | <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> | <ul><li>min: 99 characters</li><li>mean: 209.99 characters</li><li>max: 941 characters</li></ul> | <ul><li>min: 102 characters</li><li>mean: 213.87 characters</li><li>max: 981 characters</li></ul> | <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> |
+* Samples:
+  | anchor                                                                    | positive                                                                                                                                                                                                                                                                    | negative_1                                                                                                                                                                                                                                                                | negative_2                                                               |
+  |:--------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
+  | <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490</code>   | <code>This measurement was conducted with 10x 3' v3. Cell sample from the cortex of kidney, taken from a 43-year-old male of European ethnicity with a reported history of kidney cancer. The cell type is identified as a kidney collecting duct intercalated cell.</code> | <code>This measurement was conducted with 10x 3' v3. Kidney collecting duct intercalated cell from a 43-year old European male with kidney cancer, taken from the cortex of kidney and cryopreserved for further analysis.</code>                                         | <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_9</code>    |
+  | <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269</code>   | <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebellum, specifically from the Cerebellar Vermis - CBV region, with European self-reported ethnicity, analyzed at the nucleus level.</code>                                | <code>This measurement was conducted with 10x 3' v3. Endothelial cells derived from the cerebellum (specifically, cerebellar vermis) of a 42-year-old male, classified under the vascular supercluster term.</code>                                                       | <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_923</code>  |
+  | <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258</code> | <code>This measurement was conducted with 10x 5' v1. Cell sample from the tonsil of a 9-year-old female with recurrent tonsillitis, characterized as a centroblast B cell with IGLC2, IGLV7-43, IGLJ3 immunoglobulin genes expressed.</code>                                | <code>This measurement was conducted with 10x 5' v1. Centroblast cells derived from a 3-year-old male human tonsil sample, with obstructive sleep apnea and recurrent tonsillitis, undergoing affinity maturation and differentiation into memory or plasma cells.</code> | <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_9654</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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 0.05
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 0.05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | cellxgene pseudo bulk 100k multiplets natural language annotation loss | cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_cosine_accuracy |
+|:------:|:----:|:-------------:|:----------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
+| 0.3155 | 100  | 4.3241        | 22.9958                                                                | 0.5035                                                                                            |
+| 0.6309 | 200  | 3.2968        | 21.9224                                                                | 0.5026                                                                                            |
+| 0.9464 | 300  | 2.9589        | 21.0856                                                                | 0.5008                                                                                            |
+| 1.2618 | 400  | 2.7865        | 21.0013                                                                | 0.5055                                                                                            |
+| 1.5773 | 500  | 2.6859        | 19.5792                                                                | 0.5096                                                                                            |
+| 1.8927 | 600  | 2.6192        | 19.5848                                                                | 0.5105                                                                                            |
+| 2.2082 | 700  | 2.5577        | 19.8195                                                                | 0.5115                                                                                            |
+| 2.5237 | 800  | 2.4599        | 18.7359                                                                | 0.5118                                                                                            |
+| 2.8391 | 900  | 2.4419        | 18.0783                                                                | 0.5138                                                                                            |
+| 3.1546 | 1000 | 2.423         | 17.2768                                                                | 0.5156                                                                                            |
+| 3.4700 | 1100 | 2.4112        | 16.9335                                                                | 0.5164                                                                                            |
+| 3.7855 | 1200 | 2.4067        | 16.4885                                                                | 0.5158                                                                                            |
+
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 5.0.0
+- Transformers: 4.55.0.dev0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.9.0
+- Datasets: 2.19.1
+- Tokenizers: 0.21.4
+
+## 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}
+}
+```
+
+<!--
+## 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_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.55.0.dev0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,8 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_MMContextEncoder",
+    "type": "mmcontext.models.mmcontextencoder.MMContextEncoder"
+  }
+]