Update README.md

README.md

@@ -54,427 +54,111 @@ model-index:
       name: F1 Weighted
 ---
 
-# CrossEncoder based on jhu-clsp/ettin-encoder-17m
+# EttinX Cross-Encoder: Natural Language Inference (NLI)
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text pair classification.
+This cross encoder performs sequence classification for contradiction/neutral/entailment labels. This has
+drop-in compatibility with comparable sentence transformers cross encoders.
 
-## Model Details
+To train this model, I added teacher logits to the all-nli dataset `dleemiller/all-nli-distill` from the
+`dleemiller/ModernCE-large-nli` model. This significantly improves performance above standard training.
 
-### Model Description
-- **Model Type:** Cross Encoder
-- **Base model:** [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) <!-- at revision 987607455c61e7a5bbc85f7758e0512ea6d0ae4c -->
-- **Maximum Sequence Length:** 7999 tokens
-- **Number of Output Labels:** 3 labels
-- **Training Dataset:**
-    - [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill)
-- **Language:** en
-<!-- - **License:** Unknown -->
+This 17m architecture is based on ModernBERT and is an excellent candidate for lightweight **CPU inference**.
 
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
-
-## Usage
+---
 
-### Direct Usage (Sentence Transformers)
+## Features
+- **High performing:** Achieves **80.47%** and **86.95%** (Micro F1) on MNLI mismatched and SNLI test.
+- **Efficient architecture:** Based on the Ettin-17m encoder design (17M parameters), offering faster inference speeds.
+- **Extended context length:** Processes sequences up to 8192 tokens, great for LLM output evals.
 
-First install the Sentence Transformers library:
+---
 
-```bash
-pip install -U sentence-transformers
-```
+## Performance
 
-Then you can load this model and run inference.
-```python
-from sentence_transformers import CrossEncoder
+| Model                     | MNLI Mismatched   | SNLI Test    | Context Length | # Parameters |
+|---------------------------|-------------------|--------------|----------------|----------------|
+| `dleemiller/ModernCE-large-nli`      | **0.9202**        | 0.9110       | 8192           | 395M  |
+| `dleemiller/ModernCE-base-nli`       | 0.9034            | 0.9025       | 8192           | 149M  |
+| `cross-encoder/deberta-v3-large`     | 0.9049            | 0.9220       | 512            | 435M  | 
+| `cross-encoder/deberta-v3-base`      | 0.9004            | 0.9234       | 512            | 184M  |
+| `cross-encoder/nli-distilroberta-base` | 0.8398          | 0.8838       | 512            | 82M   |
+| `dleemiller/EttinX-nli-xxs`          | 0.8047            | 0.8695       | 8192           | 17M   |
 
-# Download from the 🤗 Hub
-model = CrossEncoder("cross_encoder_model_id")
-# Get scores for pairs of texts
-pairs = [
-    ['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'],
-    ['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
-    ['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'],
-    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'],
-    ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids at a ballgame wash their hands.'],
-]
-scores = model.predict(pairs)
-print(scores.shape)
-# (5, 3)
-```
 
-<!--
-### Direct Usage (Transformers)
+---
 
-<details><summary>Click to see the direct usage in Transformers</summary>
+## Usage
 
-</details>
--->
+To use EttinX for NLI tasks, you can load the model with the Hugging Face `sentence-transformers` library:
 
-<!--
-### Downstream Usage (Sentence Transformers)
+```python
+from sentence_transformers import CrossEncoder
 
-You can finetune this model on your own dataset.
+# Load EttinX model
+model = CrossEncoder("dleemiller/EttinX-nli-xxs")
 
-<details><summary>Click to expand</summary>
+scores = model.predict([
+    ('A man is eating pizza', 'A man eats something'),
+    ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')
+])
 
-</details>
--->
+# Convert scores to labels
+label_mapping = ['contradiction', 'entailment', 'neutral']
+labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
+# ['entailment', 'contradiction']
+```
 
-<!--
-### Out-of-Scope Use
+---
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+## Training Details
 
-## Evaluation
+### Pretraining
+We initialize the `` weights.
 
-### Metrics
+Details:
+- Batch size: 512
+- Learning rate: 1e-4
+- **Attention Dropout:** attention dropout 0.1
 
-#### Cross Encoder Classification
+### Fine-Tuning
+Fine-tuning was performed on the `dleemiller/all-nli-distill` dataset.
 
-* Datasets: `AllNLI-dev` and `AllNLI-test`
-* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+### Validation Results
+The model achieved the following test set micro f1 performance after fine-tuning:
+- **MNLI Unmatched:** 0.8047
+- **SNLI:** 0.8695
 
-| Metric       | AllNLI-dev | AllNLI-test |
-|:-------------|:-----------|:------------|
-| **f1_macro** | **0.8432** | **0.8443**  |
-| f1_micro     | 0.8435     | 0.8447      |
-| f1_weighted  | 0.8439     | 0.845       |
+---
 
-<!--
-## Bias, Risks and Limitations
+## Model Card
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+- **Architecture:** Ettin-encoder-17m
+- **Fine-Tuning Data:** `dleemiller/all-nli-distill`
 
-<!--
-### Recommendations
+---
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+## Thank You
 
-## Training Details
+Thanks to the Johns Hopkins team for providing the ModernBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.
 
-### Training Dataset
-
-#### all-nli-distill
-
-* Dataset: [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) at [6907d07](https://huggingface.co/datasets/dleemiller/all-nli-distill/tree/6907d071937601df154a4641e824cbce44e8fd41)
-* Size: 942,069 training samples
-* Columns: <code>premise</code>, <code>hypothesis</code>, <code>label</code>, and <code>hash</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | premise                                                                                        | hypothesis                                                                                     | label                                                              | hash                                                                                          |
-  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
-  | type    | string                                                                                         | string                                                                                         | int                                                                | string                                                                                        |
-  | details | <ul><li>min: 7 characters</li><li>mean: 87.47 characters</li><li>max: 485 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 45.98 characters</li><li>max: 157 characters</li></ul> | <ul><li>0: ~32.70%</li><li>1: ~34.20%</li><li>2: ~33.10%</li></ul> | <ul><li>min: 32 characters</li><li>mean: 32.0 characters</li><li>max: 32 characters</li></ul> |
-* Samples:
-  | premise                                                                               | hypothesis                             | label          | hash                                          |
-  |:--------------------------------------------------------------------------------------|:---------------------------------------|:---------------|:----------------------------------------------|
-  | <code>somehow, somewhere.</code>                                                      | <code>Someplace, in some way.</code>   | <code>1</code> | <code>9a14d41bdf965ed999446ea11dbf5b67</code> |
-  | <code>A boy is sitting on a boat with two flags.</code>                               | <code>A blonde person sitting.</code>  | <code>2</code> | <code>758664a444dd4c02d89220da2ab499ac</code> |
-  | <code>A asian male suit clad, uses a umbrella to shield himself from the rain.</code> | <code>He is late for a meeting.</code> | <code>2</code> | <code>7e1155728f9cf33655076ec6b36cdb10</code> |
-* Loss: <code>__main__.PrecomputedDistillationLoss</code>
-
-### Evaluation Dataset
-
-#### all-nli-distill
-
-* Dataset: [all-nli-distill](https://huggingface.co/datasets/dleemiller/all-nli-distill) at [6907d07](https://huggingface.co/datasets/dleemiller/all-nli-distill/tree/6907d071937601df154a4641e824cbce44e8fd41)
-* Size: 19,657 evaluation samples
-* Columns: <code>premise</code>, <code>hypothesis</code>, <code>label</code>, and <code>hash</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | premise                                                                                         | hypothesis                                                                                      | label                                                              | hash                                                                                          |
-  |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
-  | type    | string                                                                                          | string                                                                                          | int                                                                | string                                                                                        |
-  | details | <ul><li>min: 16 characters</li><li>mean: 75.01 characters</li><li>max: 229 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 37.66 characters</li><li>max: 116 characters</li></ul> | <ul><li>0: ~33.60%</li><li>1: ~33.10%</li><li>2: ~33.30%</li></ul> | <ul><li>min: 32 characters</li><li>mean: 32.0 characters</li><li>max: 32 characters</li></ul> |
-* Samples:
-  | premise                                                            | hypothesis                                                                                         | label          | hash                                          |
-  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------|
-  | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>2</code> | <code>ee3806dad2b757a8e131aa50f2b73ec9</code> |
-  | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code>                                                       | <code>1</code> | <code>563afee877ed42f33dafe7c76fe9604b</code> |
-  | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code>                                                  | <code>0</code> | <code>fd7c1382a8321094d60105ff37c038da</code> |
-* Loss: <code>__main__.PrecomputedDistillationLoss</code>
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 512
-- `per_device_eval_batch_size`: 512
-- `learning_rate`: 0.0002
-- `num_train_epochs`: 5
-- `warmup_ratio`: 0.1
-- `bf16`: True
-- `load_best_model_at_end`: 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`: 512
-- `per_device_eval_batch_size`: 512
-- `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`: 0.0002
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 5
-- `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`: True
-- `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}
-- `parallelism_config`: None
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch_fused
-- `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 | Validation Loss | AllNLI-dev_f1_macro | AllNLI-test_f1_macro |
-|:----------:|:--------:|:-------------:|:---------------:|:-------------------:|:--------------------:|
-| -1         | -1       | -             | -               | 0.2911              | -                    |
-| 0.0543     | 100      | 6.5112        | -               | -                   | -                    |
-| 0.1087     | 200      | 3.7062        | -               | -                   | -                    |
-| 0.1630     | 300      | 2.8158        | -               | -                   | -                    |
-| 0.2174     | 400      | 2.4929        | -               | -                   | -                    |
-| 0.2717     | 500      | 2.3007        | 2.2750          | 0.7475              | -                    |
-| 0.3261     | 600      | 2.1216        | -               | -                   | -                    |
-| 0.3804     | 700      | 1.9902        | -               | -                   | -                    |
-| 0.4348     | 800      | 1.943         | -               | -                   | -                    |
-| 0.4891     | 900      | 1.8469        | -               | -                   | -                    |
-| 0.5435     | 1000     | 1.7757        | 1.8039          | 0.7890              | -                    |
-| 0.5978     | 1100     | 1.7368        | -               | -                   | -                    |
-| 0.6522     | 1200     | 1.6685        | -               | -                   | -                    |
-| 0.7065     | 1300     | 1.598         | -               | -                   | -                    |
-| 0.7609     | 1400     | 1.5582        | -               | -                   | -                    |
-| 0.8152     | 1500     | 1.5229        | 1.5512          | 0.8052              | -                    |
-| 0.8696     | 1600     | 1.4953        | -               | -                   | -                    |
-| 0.9239     | 1700     | 1.4457        | -               | -                   | -                    |
-| 0.9783     | 1800     | 1.4274        | -               | -                   | -                    |
-| 1.0326     | 1900     | 1.2831        | -               | -                   | -                    |
-| 1.0870     | 2000     | 1.1841        | 1.4433          | 0.8147              | -                    |
-| 1.1413     | 2100     | 1.1605        | -               | -                   | -                    |
-| 1.1957     | 2200     | 1.1525        | -               | -                   | -                    |
-| 1.25       | 2300     | 1.1417        | -               | -                   | -                    |
-| 1.3043     | 2400     | 1.1635        | -               | -                   | -                    |
-| 1.3587     | 2500     | 1.1386        | 1.3484          | 0.8222              | -                    |
-| 1.4130     | 2600     | 1.1369        | -               | -                   | -                    |
-| 1.4674     | 2700     | 1.1333        | -               | -                   | -                    |
-| 1.5217     | 2800     | 1.1142        | -               | -                   | -                    |
-| 1.5761     | 2900     | 1.0981        | -               | -                   | -                    |
-| 1.6304     | 3000     | 1.1037        | 1.3646          | 0.8204              | -                    |
-| 1.6848     | 3100     | 1.0831        | -               | -                   | -                    |
-| 1.7391     | 3200     | 1.0799        | -               | -                   | -                    |
-| 1.7935     | 3300     | 1.063         | -               | -                   | -                    |
-| 1.8478     | 3400     | 1.0715        | -               | -                   | -                    |
-| 1.9022     | 3500     | 1.0707        | 1.2478          | 0.8323              | -                    |
-| 1.9565     | 3600     | 1.047         | -               | -                   | -                    |
-| 2.0109     | 3700     | 0.9925        | -               | -                   | -                    |
-| 2.0652     | 3800     | 0.7622        | -               | -                   | -                    |
-| 2.1196     | 3900     | 0.7608        | -               | -                   | -                    |
-| 2.1739     | 4000     | 0.7627        | 1.2346          | 0.8346              | -                    |
-| 2.2283     | 4100     | 0.7728        | -               | -                   | -                    |
-| 2.2826     | 4200     | 0.7674        | -               | -                   | -                    |
-| 2.3370     | 4300     | 0.7716        | -               | -                   | -                    |
-| 2.3913     | 4400     | 0.7728        | -               | -                   | -                    |
-| 2.4457     | 4500     | 0.7814        | 1.2380          | 0.8360              | -                    |
-| 2.5        | 4600     | 0.7556        | -               | -                   | -                    |
-| 2.5543     | 4700     | 0.7698        | -               | -                   | -                    |
-| 2.6087     | 4800     | 0.7643        | -               | -                   | -                    |
-| 2.6630     | 4900     | 0.765         | -               | -                   | -                    |
-| 2.7174     | 5000     | 0.7661        | 1.2012          | 0.8363              | -                    |
-| 2.7717     | 5100     | 0.7605        | -               | -                   | -                    |
-| 2.8261     | 5200     | 0.7546        | -               | -                   | -                    |
-| 2.8804     | 5300     | 0.7572        | -               | -                   | -                    |
-| 2.9348     | 5400     | 0.7568        | -               | -                   | -                    |
-| 2.9891     | 5500     | 0.7422        | 1.1767          | 0.8396              | -                    |
-| 3.0435     | 5600     | 0.5901        | -               | -                   | -                    |
-| 3.0978     | 5700     | 0.5473        | -               | -                   | -                    |
-| 3.1522     | 5800     | 0.5463        | -               | -                   | -                    |
-| 3.2065     | 5900     | 0.5453        | -               | -                   | -                    |
-| 3.2609     | 6000     | 0.5484        | 1.1911          | 0.8419              | -                    |
-| 3.3152     | 6100     | 0.5506        | -               | -                   | -                    |
-| 3.3696     | 6200     | 0.5444        | -               | -                   | -                    |
-| 3.4239     | 6300     | 0.5496        | -               | -                   | -                    |
-| 3.4783     | 6400     | 0.5489        | -               | -                   | -                    |
-| 3.5326     | 6500     | 0.5497        | 1.1816          | 0.8400              | -                    |
-| 3.5870     | 6600     | 0.5476        | -               | -                   | -                    |
-| 3.6413     | 6700     | 0.5478        | -               | -                   | -                    |
-| 3.6957     | 6800     | 0.5444        | -               | -                   | -                    |
-| 3.75       | 6900     | 0.5493        | -               | -                   | -                    |
-| 3.8043     | 7000     | 0.5422        | 1.1711          | 0.8440              | -                    |
-| 3.8587     | 7100     | 0.5434        | -               | -                   | -                    |
-| 3.9130     | 7200     | 0.5438        | -               | -                   | -                    |
-| 3.9674     | 7300     | 0.5416        | -               | -                   | -                    |
-| 4.0217     | 7400     | 0.491         | -               | -                   | -                    |
-| 4.0761     | 7500     | 0.4108        | 1.1752          | 0.8423              | -                    |
-| 4.1304     | 7600     | 0.4143        | -               | -                   | -                    |
-| 4.1848     | 7700     | 0.415         | -               | -                   | -                    |
-| 4.2391     | 7800     | 0.4118        | -               | -                   | -                    |
-| 4.2935     | 7900     | 0.4221        | -               | -                   | -                    |
-| 4.3478     | 8000     | 0.4153        | 1.1767          | 0.8436              | -                    |
-| 4.4022     | 8100     | 0.4159        | -               | -                   | -                    |
-| 4.4565     | 8200     | 0.411         | -               | -                   | -                    |
-| 4.5109     | 8300     | 0.4216        | -               | -                   | -                    |
-| 4.5652     | 8400     | 0.4163        | -               | -                   | -                    |
-| 4.6196     | 8500     | 0.4118        | 1.1720          | 0.8429              | -                    |
-| 4.6739     | 8600     | 0.4198        | -               | -                   | -                    |
-| 4.7283     | 8700     | 0.4154        | -               | -                   | -                    |
-| 4.7826     | 8800     | 0.4057        | -               | -                   | -                    |
-| 4.8370     | 8900     | 0.4098        | -               | -                   | -                    |
-| **4.8913** | **9000** | **0.4064**    | **1.1687**      | **0.8432**          | **-**                |
-| 4.9457     | 9100     | 0.4056        | -               | -                   | -                    |
-| 5.0        | 9200     | 0.4115        | -               | -                   | -                    |
-| -1         | -1       | -             | -               | -                   | 0.8443               |
-
-* The bold row denotes the saved checkpoint.
-
-### Framework Versions
-- Python: 3.12.2
-- Sentence Transformers: 5.1.0
-- Transformers: 4.57.0.dev0
-- PyTorch: 2.8.0+cu128
-- Accelerate: 1.10.1
-- Datasets: 4.0.0
-- Tokenizers: 0.22.0
+---
 
 ## Citation
 
-### BibTeX
+If you use this model in your research, please cite:
 
-#### 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",
+@misc{moderncenli2025,
+  author = {Miller, D. Lee},
+  title = {EttinX NLI: An NLI cross encoder model},
+  year = {2025},
+  publisher = {Hugging Face Hub},
+  url = {https://huggingface.co/dleemiller/EttinX-nli-xxs},
 }
 ```
 
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-
-<!--
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+## License
 
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+This model is licensed under the [MIT License](LICENSE).