Update README.md

README.md

@@ -1,128 +1,62 @@
 ---
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:438516
-- loss:CoSENTLoss
-base_model: sentence-transformers/all-mpnet-base-v2
-widget:
-- source_sentence: 'Ventral humeral ridge: or not'
-  sentences:
-  - >-
-    If metasternum ossified, shape: long, narrow and tapering markedly
-    anteriorly to posteriorly, length up to 3.5 times maximum width
-  - >-
-    Astragalus, dorsolateral margin:: overlaps the anterior and posterior
-    portions of the calcaneum equally
-  - 'Ulna size: does not apply'
-- source_sentence: >-
-    Form of distal portion of anteroventral process of ectopterygoid: varyingly
-    falcate
-  sentences:
-  - 'Middle and distal radials in dorsal and anal fins: absent'
-  - >-
-    Degree of development of primitively medial portion of fourth upper
-    pharyngeal tooth-plate: fourth upper pharyngeal tooth-plate covers ventral,
-    posterior, dorsal and sometimes anterior surfaces of fourth
-    infrapharyngobranchial
-  - 'Shape of pharyngeal apophysis (basioccipital): forked anteriorly'
-- source_sentence: >-
-    Form of distal portion of anteroventral process of ectopterygoid: varyingly
-    falcate
-  sentences:
-  - 'parhypural: present'
-  - 'Epural: heavy'
-  - 'First infraorbital: short'
-- source_sentence: >-
-    Form of distal portion of anteroventral process of ectopterygoid: varyingly
-    falcate
-  sentences:
-  - 'Dentary and angular: touch'
-  - 'Urohyal and first basibranchial: firmly attached'
-  - 'Supraneural 3-4 (nonadditive): absent'
-- source_sentence: >-
-    Form of distal portion of anteroventral process of ectopterygoid: varyingly
-    falcate
-  sentences:
-  - 'Ventral diverging lamellae of mesethmoid: lamellae reduced or absent'
-  - 'Ventral ridge of the coracoid with a posterior process: absent'
-  - 'carpals: fully or partially ossified'
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-model-index:
-- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: pheno dev
-      type: pheno-dev
-    metrics:
-    - type: pearson_cosine
-      value: 0.6082332469417436
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.6250387873495056
-      name: Spearman Cosine
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: pheno test
-      type: pheno-test
-    metrics:
-    - type: pearson_cosine
-      value: 0.6822053314599665
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.705688010939619
-      name: Spearman Cosine
 license: mit
 language:
 - en
+library_name: sentence-transformers 
+tags:
+- ontology
+- nlp
+- biology
+- animals
+- fish
+- embedding
+- trait
+datasets: 
+- imageomics/char-sim-data
+metrics: # key list: https://hf.co/metrics
+model_name: Trait2Vec
+model_description: "Language model for embedding organismal trait descriptions. Built using Sentence-Transformer architecture and trained with trait descriptions from char-sim-data."
 ---
 
-# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+# Model Card for Trait2Vec
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+Trait2Vec is a model for the tree of life, built using CLIP architecture as a language model to embed organismal trait descriptions in a way that preserves the structure induced by a semantic similarity (e.g. SimGIC). The model was trained on the [char-sim-data](https://huggingface.co/datasets/imageomics/char-sim-data/edit/main/README.md). 
+Through qualitative data exploration we observe the cosine similarity between embeddings of raw trait description is proportional to the semantic similarity of their corresponding ontological representations. 
 
 ## Model Details
 
 ### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
-- **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 256 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Jim Balhoff, Soumyashree Kar, Hilmar Lapp, Juan Garcia 
+- **Model type:** Sentence Transformer
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Fine-tuned from model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
 
 ### 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)
+- **Repository:** [Trait2Vec](https://github.com/Imageomics/char-sim/tree/main)
 
-### Full Model Architecture
+## Uses
 
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
-)
-```
+Trait2Vec has been qualitatively evaluated in the ability to embed raw trait descriptions in a way that preserves the structure of an ontology. Accordingly, we expect it to produce an alternative computational representation of the traits of an organism. 
+
+### Direct Use
+
+It can be used to embed the textual trait descriptions associated with an organism.
 
-## Usage
 
-### Direct Usage (Sentence Transformers)
+## Bias, Risks, and Limitations
+
+This model is finetuned from  [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), therefore it inherets its corresponding biases and risks. The training dataset[char-sim-data](https://huggingface.co/datasets/imageomics/char-sim-data/edit/main/README.md) introduces the biases of the single similarity metric and ontology. This means the embedding inherits that metric’s inductive biases, coverage gaps, and evolving definitions. Biological conclusions may differ under alternative metrics (e.g., Resnik, Jaccard) or other phenotype ontologies. 
+
+### Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
 
 First install the Sentence Transformers library:
 
@@ -152,62 +86,11 @@ 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.*
--->
-
-## Evaluation
-
-### Metrics
-
-#### Semantic Similarity
-
-* Datasets: `pheno-dev` and `pheno-test`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | pheno-dev | pheno-test |
-|:--------------------|:----------|:-----------|
-| pearson_cosine      | 0.6082    | 0.6822     |
-| **spearman_cosine** | **0.625** | **0.7057** |
-
-<!--
-## 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
+### Training Data
 
+This model was trained on the [char-sim-data](https://huggingface.co/datasets/imageomics/char-sim-data/edit/main/README.md) dataset.
 
 * Size: 438,516 training samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
@@ -230,11 +113,27 @@ You can finetune this model on your own dataset.
   }
   ```
 
-### Evaluation Dataset
+#### Training Hyperparameters
+#### Non-Default Hyperparameters
 
-#### Unnamed Dataset
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 1e-06
+
+- **Training regime:** fp32
 
 
+## Evaluation
+
+We tested Trait2Vec on a hold-out split of 20\% of the ['char-sim-data'](https://huggingface.co/datasets/imageomics/char-sim-data/tree/main) dataset. No descriptor overlap was ensured.
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
 * Size: 111,628 evaluation samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
 * Approximate statistics based on the first 1000 samples:
@@ -256,284 +155,57 @@ You can finetune this model on your own dataset.
   }
   ```
 
-### Training Hyperparameters
-#### Non-Default Hyperparameters
+#### Metrics
 
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `learning_rate`: 2e-05
-- `num_train_epochs`: 10
-- `warmup_ratio`: 1e-06
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
 
-#### 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`: 64
-- `per_device_eval_batch_size`: 64
-- `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`: 2e-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`: 10
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 1e-06
-- `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`: proportional
-
-</details>
-
-### Training Logs
-<details><summary>Click to expand</summary>
-
-| Epoch  | Step  | Training Loss | Validation Loss | pheno-dev_spearman_cosine | pheno-test_spearman_cosine |
-|:------:|:-----:|:-------------:|:---------------:|:-------------------------:|:--------------------------:|
-| 0.0730 | 500   | 7.3492        | -               | -                         | -                          |
-| 0.1459 | 1000  | 6.9718        | -               | -                         | -                          |
-| 0.2189 | 1500  | 6.7986        | -               | -                         | -                          |
-| 0.2919 | 2000  | 6.7157        | 8.8773          | 0.6305                    | -                          |
-| 0.3649 | 2500  | 6.6327        | -               | -                         | -                          |
-| 0.4378 | 3000  | 6.5661        | -               | -                         | -                          |
-| 0.5108 | 3500  | 6.5309        | -               | -                         | -                          |
-| 0.5838 | 4000  | 6.4737        | 10.0841         | 0.6116                    | -                          |
-| 0.6567 | 4500  | 6.4516        | -               | -                         | -                          |
-| 0.7297 | 5000  | 6.4235        | -               | -                         | -                          |
-| 0.8027 | 5500  | 6.3908        | -               | -                         | -                          |
-| 0.8757 | 6000  | 6.3602        | 10.8098         | 0.6071                    | -                          |
-| 0.9486 | 6500  | 6.3315        | -               | -                         | -                          |
-| 1.0216 | 7000  | 6.3236        | -               | -                         | -                          |
-| 1.0946 | 7500  | 6.2753        | -               | -                         | -                          |
-| 1.1675 | 8000  | 6.2845        | 11.9185         | 0.6263                    | -                          |
-| 1.2405 | 8500  | 6.254         | -               | -                         | -                          |
-| 1.3135 | 9000  | 6.2351        | -               | -                         | -                          |
-| 1.3865 | 9500  | 6.2017        | -               | -                         | -                          |
-| 1.4594 | 10000 | 6.2138        | 12.3766         | 0.6161                    | -                          |
-| 1.5324 | 10500 | 6.2066        | -               | -                         | -                          |
-| 1.6054 | 11000 | 6.1834        | -               | -                         | -                          |
-| 1.6783 | 11500 | 6.1937        | -               | -                         | -                          |
-| 1.7513 | 12000 | 6.1661        | 12.9426         | 0.6113                    | -                          |
-| 1.8243 | 12500 | 6.1362        | -               | -                         | -                          |
-| 1.8973 | 13000 | 6.1065        | -               | -                         | -                          |
-| 1.9702 | 13500 | 6.1371        | -               | -                         | -                          |
-| 2.0432 | 14000 | 6.0983        | 13.5966         | 0.6156                    | -                          |
-| 2.1162 | 14500 | 6.0978        | -               | -                         | -                          |
-| 2.1891 | 15000 | 6.0767        | -               | -                         | -                          |
-| 2.2621 | 15500 | 6.066         | -               | -                         | -                          |
-| 2.3351 | 16000 | 6.0739        | 13.9316         | 0.6260                    | -                          |
-| 2.4081 | 16500 | 6.0635        | -               | -                         | -                          |
-| 2.4810 | 17000 | 6.0616        | -               | -                         | -                          |
-| 2.5540 | 17500 | 6.0219        | -               | -                         | -                          |
-| 2.6270 | 18000 | 6.0129        | 14.3098         | 0.6158                    | -                          |
-| 2.6999 | 18500 | 6.0414        | -               | -                         | -                          |
-| 2.7729 | 19000 | 6.0317        | -               | -                         | -                          |
-| 2.8459 | 19500 | 6.0158        | -               | -                         | -                          |
-| 2.9189 | 20000 | 6.0078        | 14.6487         | 0.6188                    | -                          |
-| 2.9918 | 20500 | 6.0295        | -               | -                         | -                          |
-| 3.0648 | 21000 | 5.9664        | -               | -                         | -                          |
-| 3.1378 | 21500 | 5.9682        | -               | -                         | -                          |
-| 3.2107 | 22000 | 5.9755        | 15.2314         | 0.6202                    | -                          |
-| 3.2837 | 22500 | 5.9608        | -               | -                         | -                          |
-| 3.3567 | 23000 | 5.9469        | -               | -                         | -                          |
-| 3.4297 | 23500 | 5.9673        | -               | -                         | -                          |
-| 3.5026 | 24000 | 5.9496        | 15.4385         | 0.6237                    | -                          |
-| 3.5756 | 24500 | 5.9148        | -               | -                         | -                          |
-| 3.6486 | 25000 | 5.9568        | -               | -                         | -                          |
-| 3.7215 | 25500 | 5.9135        | -               | -                         | -                          |
-| 3.7945 | 26000 | 5.9363        | 15.3029         | 0.6217                    | -                          |
-| 3.8675 | 26500 | 5.9096        | -               | -                         | -                          |
-| 3.9405 | 27000 | 5.9171        | -               | -                         | -                          |
-| 4.0134 | 27500 | 5.8955        | -               | -                         | -                          |
-| 4.0864 | 28000 | 5.861         | 15.3221         | 0.6265                    | -                          |
-| 4.1594 | 28500 | 5.8726        | -               | -                         | -                          |
-| 4.2323 | 29000 | 5.8835        | -               | -                         | -                          |
-| 4.3053 | 29500 | 5.8823        | -               | -                         | -                          |
-| 4.3783 | 30000 | 5.8702        | 15.7276         | 0.6266                    | -                          |
-| 4.4513 | 30500 | 5.8721        | -               | -                         | -                          |
-| 4.5242 | 31000 | 5.8988        | -               | -                         | -                          |
-| 4.5972 | 31500 | 5.8671        | -               | -                         | -                          |
-| 4.6702 | 32000 | 5.8705        | 15.9223         | 0.6212                    | -                          |
-| 4.7431 | 32500 | 5.8905        | -               | -                         | -                          |
-| 4.8161 | 33000 | 5.8634        | -               | -                         | -                          |
-| 4.8891 | 33500 | 5.8637        | -               | -                         | -                          |
-| 4.9621 | 34000 | 5.8385        | 16.1225         | 0.6045                    | -                          |
-| 5.0350 | 34500 | 5.8583        | -               | -                         | -                          |
-| 5.1080 | 35000 | 5.821         | -               | -                         | -                          |
-| 5.1810 | 35500 | 5.8219        | -               | -                         | -                          |
-| 5.2539 | 36000 | 5.8367        | 15.6937         | 0.6240                    | -                          |
-| 5.3269 | 36500 | 5.8245        | -               | -                         | -                          |
-| 5.3999 | 37000 | 5.8161        | -               | -                         | -                          |
-| 5.4729 | 37500 | 5.8138        | -               | -                         | -                          |
-| 5.5458 | 38000 | 5.815         | 15.7507         | 0.6279                    | -                          |
-| 5.6188 | 38500 | 5.8238        | -               | -                         | -                          |
-| 5.6918 | 39000 | 5.8235        | -               | -                         | -                          |
-| 5.7647 | 39500 | 5.8407        | -               | -                         | -                          |
-| 5.8377 | 40000 | 5.8258        | 15.8875         | 0.6213                    | -                          |
-| 5.9107 | 40500 | 5.7941        | -               | -                         | -                          |
-| 5.9837 | 41000 | 5.8301        | -               | -                         | -                          |
-| 6.0566 | 41500 | 5.7734        | -               | -                         | -                          |
-| 6.1296 | 42000 | 5.7759        | 16.0155         | 0.6212                    | -                          |
-| 6.2026 | 42500 | 5.7951        | -               | -                         | -                          |
-| 6.2755 | 43000 | 5.8023        | -               | -                         | -                          |
-| 6.3485 | 43500 | 5.7848        | -               | -                         | -                          |
-| 6.4215 | 44000 | 5.7774        | 16.0796         | 0.6152                    | -                          |
-| 6.4945 | 44500 | 5.7719        | -               | -                         | -                          |
-| 6.5674 | 45000 | 5.7822        | -               | -                         | -                          |
-| 6.6404 | 45500 | 5.7734        | -               | -                         | -                          |
-| 6.7134 | 46000 | 5.7856        | 16.2461         | 0.6142                    | -                          |
-| 6.7863 | 46500 | 5.7949        | -               | -                         | -                          |
-| 6.8593 | 47000 | 5.8346        | -               | -                         | -                          |
-| 6.9323 | 47500 | 5.7606        | -               | -                         | -                          |
-| 7.0053 | 48000 | 5.7839        | 16.0556         | 0.6249                    | -                          |
-| 7.0782 | 48500 | 5.7581        | -               | -                         | -                          |
-| 7.1512 | 49000 | 5.7472        | -               | -                         | -                          |
-| 7.2242 | 49500 | 5.7443        | -               | -                         | -                          |
-| 7.2971 | 50000 | 5.7481        | 16.1126         | 0.6248                    | -                          |
-| 7.3701 | 50500 | 5.7487        | -               | -                         | -                          |
-| 7.4431 | 51000 | 5.7443        | -               | -                         | -                          |
-| 7.5161 | 51500 | 5.76          | -               | -                         | -                          |
-| 7.5890 | 52000 | 5.7353        | 16.0932         | 0.6312                    | -                          |
-| 7.6620 | 52500 | 5.7632        | -               | -                         | -                          |
-| 7.7350 | 53000 | 5.7788        | -               | -                         | -                          |
-| 7.8079 | 53500 | 5.758         | -               | -                         | -                          |
-| 7.8809 | 54000 | 5.7324        | 16.1470         | 0.6247                    | -                          |
-| 7.9539 | 54500 | 5.7425        | -               | -                         | -                          |
-| 8.0269 | 55000 | 5.7416        | -               | -                         | -                          |
-| 8.0998 | 55500 | 5.7696        | -               | -                         | -                          |
-| 8.1728 | 56000 | 5.7493        | 16.2547         | 0.6313                    | -                          |
-| 8.2458 | 56500 | 5.7348        | -               | -                         | -                          |
-| 8.3187 | 57000 | 5.7173        | -               | -                         | -                          |
-| 8.3917 | 57500 | 5.7215        | -               | -                         | -                          |
-| 8.4647 | 58000 | 5.7163        | 16.3313         | 0.6237                    | -                          |
-| 8.5377 | 58500 | 5.722         | -               | -                         | -                          |
-| 8.6106 | 59000 | 5.7292        | -               | -                         | -                          |
-| 8.6836 | 59500 | 5.7295        | -               | -                         | -                          |
-| 8.7566 | 60000 | 5.7267        | 16.3434         | 0.6261                    | -                          |
-| 8.8295 | 60500 | 5.7207        | -               | -                         | -                          |
-| 8.9025 | 61000 | 5.7252        | -               | -                         | -                          |
-| 8.9755 | 61500 | 5.7061        | -               | -                         | -                          |
-| 9.0485 | 62000 | 5.7113        | 16.2999         | 0.6279                    | -                          |
-| 9.1214 | 62500 | 5.695         | -               | -                         | -                          |
-| 9.1944 | 63000 | 5.7152        | -               | -                         | -                          |
-| 9.2674 | 63500 | 5.7045        | -               | -                         | -                          |
-| 9.3403 | 64000 | 5.6907        | 16.2782         | 0.6264                    | -                          |
-| 9.4133 | 64500 | 5.7185        | -               | -                         | -                          |
-| 9.4863 | 65000 | 5.6903        | -               | -                         | -                          |
-| 9.5593 | 65500 | 5.705         | -               | -                         | -                          |
-| 9.6322 | 66000 | 5.7165        | 16.3625         | 0.6249                    | -                          |
-| 9.7052 | 66500 | 5.7027        | -               | -                         | -                          |
-| 9.7782 | 67000 | 5.7048        | -               | -                         | -                          |
-| 9.8511 | 67500 | 5.728         | -               | -                         | -                          |
-| 9.9241 | 68000 | 5.7111        | 16.3087         | 0.6250                    | -                          |
-| 9.9971 | 68500 | 5.7144        | -               | -                         | -                          |
-| 10.0   | 68520 | -             | -               | -                         | 0.7057                     |
-
-</details>
-
-### Framework Versions
+### Results
+
+| Metric              | Validation set | Test set |
+|:--------------------|:----------|:-----------|
+| pearson_cosine      | 0.6082    | 0.6822     |
+| **spearman_cosine** | **0.625** | **0.7057** |
+
+#### Summary
+
+Trait2Vec embeds organismal trait descriptors in a way that preserves some of the ranking structure induced by the similarity metric of the ontology.
+
+## Environmental Impact
+
+Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO$_2$eq/kWh. A cumulative of 20 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
+
+Total emissions are estimated to be 2.16 kgCO$_2$eq of which 0 percents were directly offset.
+
+Estimations were conducted using the [MachineLearning Impact calculator](https://mlco2.github.io/impact#compute) presented in:
+```bibtex
+@article{lacoste2019quantifying,
+  title={Quantifying the Carbon Emissions of Machine Learning},
+  author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, Thomas},
+  journal={arXiv preprint arXiv:1910.09700},
+  year={2019}
+}
+```
+
+### Model Architecture and Objective
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### Software
+
 - Python: 3.10.16
 - Sentence Transformers: 3.3.1
 - Transformers: 4.48.1
@@ -544,8 +216,7 @@ You can finetune this model on your own dataset.
 
 ## Citation
 
-### BibTeX
-
+**BibTeX:**
 #### Sentence Transformers
 ```bibtex
 @inproceedings{reimers-2019-sentence-bert,
@@ -570,20 +241,15 @@ You can finetune this model on your own dataset.
 }
 ```
 
-<!--
-## Glossary
 
-*Clearly define terms in order to be accessible across audiences.*
--->
+## Acknowledgements
+
+This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
-<!--
 ## Model Card Authors
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+Juan Garcia
 
-<!--
 ## Model Card Contact
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+[jjgarcia@cs.unc.edu](mailto:jjgarcia@cs.unc.edu)