Add new CrossEncoder model

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README.md +514 -0
config.json +56 -0
model.safetensors +3 -0
special_tokens_map.json +37 -0
tokenizer.json +0 -0
tokenizer_config.json +945 -0

README.md ADDED Viewed

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:578402
+- loss:BinaryCrossEntropyLoss
+base_model: answerdotai/ModernBERT-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ModernBERT-base trained on GooAQ
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: gooaq dev
+      type: gooaq-dev
+    metrics:
+    - type: map
+      value: 0.7258
+      name: Map
+    - type: mrr@10
+      value: 0.7245
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.7686
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.4807
+      name: Map
+    - type: mrr@10
+      value: 0.4689
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5499
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3866
+      name: Map
+    - type: mrr@10
+      value: 0.6058
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4233
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.5595
+      name: Map
+    - type: mrr@10
+      value: 0.5752
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6191
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.4756
+      name: Map
+    - type: mrr@10
+      value: 0.55
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5308
+      name: Ndcg@10
+---
+# ModernBERT-base trained on GooAQ
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+## Model Details
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+### 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("akr2002/reranker-ModernBERT-base-gooaq-bce")
+# Get scores for pairs of texts
+pairs = [
+    ['how do you find mass?', "Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass."],
+    ['how do you find mass?', "In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers."],
+    ['how do you find mass?', 'A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV.'],
+    ['how do you find mass?', 'Mass is the amount of matter in a body, normally measured in grams or kilograms etc. Weight is a force that pulls on a mass and is measured in Newtons. ... Density basically means how much mass is occupied in a specific volume or space. Different materials of the same size may have different masses because of its density.'],
+    ['how do you find mass?', 'Receiver – Mass communication is the transmission of the message to a large number of recipients. This mass of receivers, are often called as mass audience. The Mass audience is large, heterogenous and anonymous in nature. The receivers are scattered across a given village, state or country.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'how do you find mass?',
+    [
+        "Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass.",
+        "In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers.",
+        'A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV.',
+        'Mass is the amount of matter in a body, normally measured in grams or kilograms etc. Weight is a force that pulls on a mass and is measured in Newtons. ... Density basically means how much mass is occupied in a specific volume or space. Different materials of the same size may have different masses because of its density.',
+        'Receiver – Mass communication is the transmission of the message to a large number of recipients. This mass of receivers, are often called as mass audience. The Mass audience is large, heterogenous and anonymous in nature. The receivers are scattered across a given village, state or country.',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+<!--
+### 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 Reranking
+* Dataset: `gooaq-dev`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": false
+  }
+  ```
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.7258 (+0.1946)     |
+| mrr@10      | 0.7245 (+0.2005)     |
+| **ndcg@10** | **0.7686 (+0.1774)** |
+#### Cross Encoder Reranking
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.4807 (-0.0089)     | 0.3866 (+0.1256)     | 0.5595 (+0.1399)     |
+| mrr@10      | 0.4689 (-0.0086)     | 0.6058 (+0.1060)     | 0.5752 (+0.1485)     |
+| **ndcg@10** | **0.5499 (+0.0095)** | **0.4233 (+0.0982)** | **0.6191 (+0.1184)** |
+#### Cross Encoder Nano BEIR
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.4756 (+0.0855)     |
+| mrr@10      | 0.5500 (+0.0820)     |
+| **ndcg@10** | **0.5308 (+0.0754)** |
+<!--
+## Bias, Risks and Limitations
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+<!--
+### Recommendations
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+## Training Details
+### Training Dataset
+#### Unnamed Dataset
+* Size: 578,402 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                                       | answer                                                                                           | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | int                                             |
+  | details | <ul><li>min: 17 characters</li><li>mean: 44.75 characters</li><li>max: 84 characters</li></ul> | <ul><li>min: 54 characters</li><li>mean: 252.51 characters</li><li>max: 388 characters</li></ul> | <ul><li>0: ~83.00%</li><li>1: ~17.00%</li></ul> |
+* Samples:
+  | question                           | answer                                                                                                                                                                                                                                                                                                                               | label          |
+  |:-----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>how do you find mass?</code> | <code>Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass.</code> | <code>1</code> |
+  | <code>how do you find mass?</code> | <code>In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers.</code>                                  | <code>0</code> |
+  | <code>how do you find mass?</code> | <code>A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV.</code>                                    | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": 5
+  }
+  ```
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 4
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 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`: 1
+- `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`: 12
+- `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`: 4
+- `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}
+- `tp_size`: 0
+- `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
+| Epoch      | Step      | Training Loss | gooaq-dev_ndcg@10    | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:----------:|:---------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1         | -1        | -             | 0.1474 (-0.4438)     | 0.0356 (-0.5048)         | 0.2344 (-0.0907)          | 0.0268 (-0.4739)     | 0.0989 (-0.3564)           |
+| 0.0000     | 1         | 1.1353        | -                    | -                        | -                         | -                    | -                          |
+| 0.0277     | 1000      | 1.1797        | -                    | -                        | -                         | -                    | -                          |
+| 0.0553     | 2000      | 0.8539        | -                    | -                        | -                         | -                    | -                          |
+| 0.0830     | 3000      | 0.7438        | -                    | -                        | -                         | -                    | -                          |
+| 0.1106     | 4000      | 0.7296        | 0.7119 (+0.1206)     | 0.5700 (+0.0296)         | 0.3410 (+0.0160)          | 0.6012 (+0.1005)     | 0.5041 (+0.0487)           |
+| 0.1383     | 5000      | 0.6705        | -                    | -                        | -                         | -                    | -                          |
+| 0.1660     | 6000      | 0.6624        | -                    | -                        | -                         | -                    | -                          |
+| 0.1936     | 7000      | 0.6685        | -                    | -                        | -                         | -                    | -                          |
+| 0.2213     | 8000      | 0.6305        | 0.7328 (+0.1415)     | 0.5504 (+0.0099)         | 0.4056 (+0.0805)          | 0.6947 (+0.1941)     | 0.5502 (+0.0948)           |
+| 0.2490     | 9000      | 0.6353        | -                    | -                        | -                         | -                    | -                          |
+| 0.2766     | 10000     | 0.6118        | -                    | -                        | -                         | -                    | -                          |
+| 0.3043     | 11000     | 0.6097        | -                    | -                        | -                         | -                    | -                          |
+| 0.3319     | 12000     | 0.6003        | 0.7423 (+0.1510)     | 0.5817 (+0.0413)         | 0.3817 (+0.0566)          | 0.6152 (+0.1145)     | 0.5262 (+0.0708)           |
+| 0.3596     | 13000     | 0.5826        | -                    | -                        | -                         | -                    | -                          |
+| 0.3873     | 14000     | 0.5935        | -                    | -                        | -                         | -                    | -                          |
+| 0.4149     | 15000     | 0.5826        | -                    | -                        | -                         | -                    | -                          |
+| 0.4426     | 16000     | 0.5723        | 0.7557 (+0.1645)     | 0.5453 (+0.0049)         | 0.4029 (+0.0779)          | 0.6260 (+0.1253)     | 0.5247 (+0.0693)           |
+| 0.4702     | 17000     | 0.582         | -                    | -                        | -                         | -                    | -                          |
+| 0.4979     | 18000     | 0.5631        | -                    | -                        | -                         | -                    | -                          |
+| 0.5256     | 19000     | 0.5705        | -                    | -                        | -                         | -                    | -                          |
+| 0.5532     | 20000     | 0.544         | 0.7604 (+0.1692)     | 0.5636 (+0.0232)         | 0.4112 (+0.0862)          | 0.6260 (+0.1253)     | 0.5336 (+0.0782)           |
+| 0.5809     | 21000     | 0.5289        | -                    | -                        | -                         | -                    | -                          |
+| 0.6086     | 22000     | 0.5431        | -                    | -                        | -                         | -                    | -                          |
+| 0.6362     | 23000     | 0.5449        | -                    | -                        | -                         | -                    | -                          |
+| 0.6639     | 24000     | 0.5338        | 0.7608 (+0.1696)     | 0.5384 (-0.0020)         | 0.4327 (+0.1077)          | 0.5906 (+0.0899)     | 0.5206 (+0.0652)           |
+| 0.6915     | 25000     | 0.5401        | -                    | -                        | -                         | -                    | -                          |
+| 0.7192     | 26000     | 0.5535        | -                    | -                        | -                         | -                    | -                          |
+| 0.7469     | 27000     | 0.5353        | -                    | -                        | -                         | -                    | -                          |
+| 0.7745     | 28000     | 0.5157        | 0.7635 (+0.1723)     | 0.5217 (-0.0188)         | 0.4171 (+0.0921)          | 0.5543 (+0.0537)     | 0.4977 (+0.0423)           |
+| 0.8022     | 29000     | 0.5153        | -                    | -                        | -                         | -                    | -                          |
+| 0.8299     | 30000     | 0.5122        | -                    | -                        | -                         | -                    | -                          |
+| 0.8575     | 31000     | 0.5108        | -                    | -                        | -                         | -                    | -                          |
+| 0.8852     | 32000     | 0.5303        | 0.7685 (+0.1773)     | 0.5538 (+0.0134)         | 0.4147 (+0.0897)          | 0.6155 (+0.1149)     | 0.5280 (+0.0727)           |
+| 0.9128     | 33000     | 0.5363        | -                    | -                        | -                         | -                    | -                          |
+| 0.9405     | 34000     | 0.4996        | -                    | -                        | -                         | -                    | -                          |
+| 0.9682     | 35000     | 0.5193        | -                    | -                        | -                         | -                    | -                          |
+| **0.9958** | **36000** | **0.4995**    | **0.7686 (+0.1774)** | **0.5499 (+0.0095)**     | **0.4233 (+0.0982)**      | **0.6191 (+0.1184)** | **0.5308 (+0.0754)**       |
+| -1         | -1        | -             | 0.7686 (+0.1774)     | 0.5499 (+0.0095)         | 0.4233 (+0.0982)          | 0.6191 (+0.1184)     | 0.5308 (+0.0754)           |
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.12.7
+- Sentence Transformers: 4.0.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+## Citation
+### BibTeX
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+<!--
+## Glossary
+*Clearly define terms in order to be accessible across audiences.*
+-->
+<!--
+## Model Card Authors
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+<!--
+## Model Card Contact
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json ADDED Viewed

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