dleemiller
/

EttinX-nli-xxs

@@ -54,446 +54,110 @@ model-index:
       name: F1 Weighted
 ---
-# CrossEncoder based on jhu-clsp/ettin-encoder-17m
-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.
-## Model Details
-### 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 -->
-### 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)
-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 CrossEncoder
-# 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>
-</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
-#### Cross Encoder Classification
-* 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)
-| Metric       | AllNLI-dev | AllNLI-test |
-|:-------------|:-----------|:------------|
-| **f1_macro** | **0.8355** | **0.8411**  |
-| f1_micro     | 0.8359     | 0.8415      |
-| f1_weighted  | 0.8362     | 0.8418      |
-<!--
-## 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
-#### 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`: 0.0001
-- `num_train_epochs`: 6
-- `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`: 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`: 0.0001
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 6
-- `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}
-- `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
-<details><summary>Click to expand</summary>
-| Epoch  | Step  | Training Loss | Validation Loss | AllNLI-dev_f1_macro | AllNLI-test_f1_macro |
-|:------:|:-----:|:-------------:|:---------------:|:-------------------:|:--------------------:|
-| -1     | -1    | -             | -               | 0.2782              | -                    |
-| 0.0543 | 100   | 6.7398        | -               | -                   | -                    |
-| 0.1087 | 200   | 4.797         | -               | -                   | -                    |
-| 0.1630 | 300   | 3.277         | -               | -                   | -                    |
-| 0.2174 | 400   | 2.8003        | -               | -                   | -                    |
-| 0.2717 | 500   | 2.5854        | 2.5302          | 0.7257              | -                    |
-| 0.3261 | 600   | 2.3873        | -               | -                   | -                    |
-| 0.3804 | 700   | 2.238         | -               | -                   | -                    |
-| 0.4348 | 800   | 2.1506        | -               | -                   | -                    |
-| 0.4891 | 900   | 2.0302        | -               | -                   | -                    |
-| 0.5435 | 1000  | 1.9461        | 1.9746          | 0.7766              | -                    |
-| 0.5978 | 1100  | 1.8948        | -               | -                   | -                    |
-| 0.6522 | 1200  | 1.8219        | -               | -                   | -                    |
-| 0.7065 | 1300  | 1.7425        | -               | -                   | -                    |
-| 0.7609 | 1400  | 1.6744        | -               | -                   | -                    |
-| 0.8152 | 1500  | 1.6372        | 1.6647          | 0.8001              | -                    |
-| 0.8696 | 1600  | 1.605         | -               | -                   | -                    |
-| 0.9239 | 1700  | 1.5654        | -               | -                   | -                    |
-| 0.9783 | 1800  | 1.5148        | -               | -                   | -                    |
-| 1.0326 | 1900  | 1.4017        | -               | -                   | -                    |
-| 1.0870 | 2000  | 1.3142        | 1.5475          | 0.8060              | -                    |
-| 1.1413 | 2100  | 1.2858        | -               | -                   | -                    |
-| 1.1957 | 2200  | 1.2796        | -               | -                   | -                    |
-| 1.25   | 2300  | 1.2624        | -               | -                   | -                    |
-| 1.3043 | 2400  | 1.2757        | -               | -                   | -                    |
-| 1.3587 | 2500  | 1.2399        | 1.4343          | 0.8170              | -                    |
-| 1.4130 | 2600  | 1.25          | -               | -                   | -                    |
-| 1.4674 | 2700  | 1.2519        | -               | -                   | -                    |
-| 1.5217 | 2800  | 1.2179        | -               | -                   | -                    |
-| 1.5761 | 2900  | 1.2035        | -               | -                   | -                    |
-| 1.6304 | 3000  | 1.2185        | 1.3897          | 0.8223              | -                    |
-| 1.6848 | 3100  | 1.1846        | -               | -                   | -                    |
-| 1.7391 | 3200  | 1.1885        | -               | -                   | -                    |
-| 1.7935 | 3300  | 1.1544        | -               | -                   | -                    |
-| 1.8478 | 3400  | 1.1699        | -               | -                   | -                    |
-| 1.9022 | 3500  | 1.1654        | 1.3178          | 0.8279              | -                    |
-| 1.9565 | 3600  | 1.1577        | -               | -                   | -                    |
-| 2.0109 | 3700  | 1.1           | -               | -                   | -                    |
-| 2.0652 | 3800  | 0.8862        | -               | -                   | -                    |
-| 2.1196 | 3900  | 0.8853        | -               | -                   | -                    |
-| 2.1739 | 4000  | 0.8899        | 1.3069          | 0.8282              | -                    |
-| 2.2283 | 4100  | 0.8951        | -               | -                   | -                    |
-| 2.2826 | 4200  | 0.8869        | -               | -                   | -                    |
-| 2.3370 | 4300  | 0.8773        | -               | -                   | -                    |
-| 2.3913 | 4400  | 0.8986        | -               | -                   | -                    |
-| 2.4457 | 4500  | 0.8936        | 1.3049          | 0.8314              | -                    |
-| 2.5    | 4600  | 0.8827        | -               | -                   | -                    |
-| 2.5543 | 4700  | 0.9018        | -               | -                   | -                    |
-| 2.6087 | 4800  | 0.8841        | -               | -                   | -                    |
-| 2.6630 | 4900  | 0.8909        | -               | -                   | -                    |
-| 2.7174 | 5000  | 0.8971        | 1.2616          | 0.8318              | -                    |
-| 2.7717 | 5100  | 0.8851        | -               | -                   | -                    |
-| 2.8261 | 5200  | 0.8795        | -               | -                   | -                    |
-| 2.8804 | 5300  | 0.8793        | -               | -                   | -                    |
-| 2.9348 | 5400  | 0.8827        | -               | -                   | -                    |
-| 2.9891 | 5500  | 0.867         | 1.2425          | 0.8338              | -                    |
-| 3.0435 | 5600  | 0.7091        | -               | -                   | -                    |
-| 3.0978 | 5700  | 0.664         | -               | -                   | -                    |
-| 3.1522 | 5800  | 0.6576        | -               | -                   | -                    |
-| 3.2065 | 5900  | 0.6732        | -               | -                   | -                    |
-| 3.2609 | 6000  | 0.6755        | 1.2826          | 0.8342              | -                    |
-| 3.3152 | 6100  | 0.6762        | -               | -                   | -                    |
-| 3.3696 | 6200  | 0.6677        | -               | -                   | -                    |
-| 3.4239 | 6300  | 0.6869        | -               | -                   | -                    |
-| 3.4783 | 6400  | 0.6807        | -               | -                   | -                    |
-| 3.5326 | 6500  | 0.6759        | 1.2734          | 0.8336              | -                    |
-| 3.5870 | 6600  | 0.6781        | -               | -                   | -                    |
-| 3.6413 | 6700  | 0.678         | -               | -                   | -                    |
-| 3.6957 | 6800  | 0.678         | -               | -                   | -                    |
-| 3.75   | 6900  | 0.6766        | -               | -                   | -                    |
-| 3.8043 | 7000  | 0.6765        | 1.2607          | 0.8362              | -                    |
-| 3.8587 | 7100  | 0.6706        | -               | -                   | -                    |
-| 3.9130 | 7200  | 0.6811        | -               | -                   | -                    |
-| 3.9674 | 7300  | 0.6714        | -               | -                   | -                    |
-| 4.0217 | 7400  | 0.6232        | -               | -                   | -                    |
-| 4.0761 | 7500  | 0.5231        | 1.2781          | 0.8358              | -                    |
-| 4.1304 | 7600  | 0.529         | -               | -                   | -                    |
-| 4.1848 | 7700  | 0.526         | -               | -                   | -                    |
-| 4.2391 | 7800  | 0.5348        | -               | -                   | -                    |
-| 4.2935 | 7900  | 0.5381        | -               | -                   | -                    |
-| 4.3478 | 8000  | 0.5309        | 1.2760          | 0.8363              | -                    |
-| 4.4022 | 8100  | 0.5401        | -               | -                   | -                    |
-| 4.4565 | 8200  | 0.5323        | -               | -                   | -                    |
-| 4.5109 | 8300  | 0.5391        | -               | -                   | -                    |
-| 4.5652 | 8400  | 0.5409        | -               | -                   | -                    |
-| 4.6196 | 8500  | 0.5389        | 1.2844          | 0.8377              | -                    |
-| 4.6739 | 8600  | 0.542         | -               | -                   | -                    |
-| 4.7283 | 8700  | 0.5388        | -               | -                   | -                    |
-| 4.7826 | 8800  | 0.5289        | -               | -                   | -                    |
-| 4.8370 | 8900  | 0.5327        | -               | -                   | -                    |
-| 4.8913 | 9000  | 0.5323        | 1.2743          | 0.8350              | -                    |
-| 4.9457 | 9100  | 0.5326        | -               | -                   | -                    |
-| 5.0    | 9200  | 0.5358        | -               | -                   | -                    |
-| 5.0543 | 9300  | 0.4469        | -               | -                   | -                    |
-| 5.1087 | 9400  | 0.4526        | -               | -                   | -                    |
-| 5.1630 | 9500  | 0.4461        | 1.2887          | 0.8360              | -                    |
-| 5.2174 | 9600  | 0.4476        | -               | -                   | -                    |
-| 5.2717 | 9700  | 0.4442        | -               | -                   | -                    |
-| 5.3261 | 9800  | 0.4508        | -               | -                   | -                    |
-| 5.3804 | 9900  | 0.4456        | -               | -                   | -                    |
-| 5.4348 | 10000 | 0.4452        | 1.2967          | 0.8353              | -                    |
-| 5.4891 | 10100 | 0.4447        | -               | -                   | -                    |
-| 5.5435 | 10200 | 0.4433        | -               | -                   | -                    |
-| 5.5978 | 10300 | 0.438         | -               | -                   | -                    |
-| 5.6522 | 10400 | 0.4437        | -               | -                   | -                    |
-| 5.7065 | 10500 | 0.4428        | 1.2847          | 0.8365              | -                    |
-| 5.7609 | 10600 | 0.4379        | -               | -                   | -                    |
-| 5.8152 | 10700 | 0.4451        | -               | -                   | -                    |
-| 5.8696 | 10800 | 0.4444        | -               | -                   | -                    |
-| 5.9239 | 10900 | 0.4474        | -               | -                   | -                    |
-| 5.9783 | 11000 | 0.4461        | 1.2833          | 0.8355              | -                    |
-| -1     | -1    | -             | -               | -                   | 0.8411               |
-</details>
-### 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
-#### Sentence Transformers
 ```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
 }
 ```
-<!--
-## Glossary
-*Clearly define terms in order to be accessible across audiences.*
--->
-<!--
-## Model Card Authors
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-<!--
-## Model Card Contact
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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       name: F1 Weighted
 ---
+# EttinX Cross-Encoder: Natural Language Inference (NLI)
+This cross encoder performs sequence classification for contradiction/neutral/entailment labels. This has
+drop-in compatibility with comparable sentence transformers cross encoders.
+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.
+This 17m architecture is based on ModernBERT and is an excellent candidate for lightweight CPU inference.
+---
+## Features
+- **High performing:** Achieves **80.19%** and 86.50% 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.
+---
+## Performance
+| Model                     | MNLI Mismatched   | SNLI Test    | Context Length |
+|---------------------------|-------------------|--------------|----------------|
+| `dleemiller/ModernCE-large-nli`      | **0.9202**        | 0.9110       | 8192           |
+| `dleemiller/ModernCE-base-nli`       | 0.9034            | 0.9025       | 8192           |
+| `cross-encoders/deberta-v3-large`    | 0.9049            | 0.9220       | 512            |
+| `cross-encoders/deberta-v3-base`     | 0.9004            | 0.9234       | 512            |
+| `dleemillerEttinX-nli-sts`           | 0.8019            | 0.8650       | 8192           |
+---
+## Usage
+To use EttinX for NLI tasks, you can load the model with the Hugging Face `sentence-transformers` library:
+```python
+from sentence_transformers import CrossEncoder
+# Load EttinX model
+model = CrossEncoder("dleemiller/EttinX-nli-xxs")
+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.')
+])
+# Convert scores to labels
+label_mapping = ['contradiction', 'entailment', 'neutral']
+labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
+# ['entailment', 'contradiction']
+```
+---
+## Training Details
+### Pretraining
+We initialize the `` weights.
+Details:
+- Batch size: 512
+- Learning rate: 1e-4
+- **Attention Dropout:** attention dropout 0.1
+### Fine-Tuning
+Fine-tuning was performed on the `dleemiller/all-nli-distill` dataset.
+### Validation Results
+The model achieved the following test set performance after fine-tuning:
+- **MNLI Unmatched:** 0.8019
+- **SNLI:** 0.8650
+---
+## Model Card
+- **Architecture:** Ettin-encoder-17m
+- **Fine-Tuning Data:** `dleemiller/all-nli-distill`
+---
+## Thank You
+Thanks to the Johns Hopkins team for providing the ModernBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.
+---
 ## Citation
+If you use this model in your research, please cite:
 ```bibtex
+@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},
 }
 ```
+---
+## License
+This model is licensed under the [MIT License](LICENSE).