Add paper and code links to model card

README.md

@@ -1,20 +1,22 @@
 ---
+base_model: rasyosef/roberta-base-amharic
+datasets:
+- rasyosef/Amharic-Passage-Retrieval-Dataset-V2
 language:
 - am
+library_name: sentence-transformers
 license: mit
+metrics:
+- map
+- mrr@10
+- ndcg@10
+pipeline_tag: text-ranking
 tags:
 - sentence-transformers
 - cross-encoder
 - generated_from_trainer
 - dataset_size:491752
 - loss:BinaryCrossEntropyLoss
-base_model: rasyosef/roberta-base-amharic
-pipeline_tag: text-ranking
-library_name: sentence-transformers
-metrics:
-- map
-- mrr@10
-- ndcg@10
 model-index:
 - name: reranker-amharic-base
   results:
@@ -31,22 +33,23 @@ model-index:
     - type: ndcg@10
       value: 0.856
       name: Ndcg@10
-datasets:
-- rasyosef/Amharic-Passage-Retrieval-Dataset-V2
 ---
 
 # reranker-amharic-base
 
 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [rasyosef/roberta-base-amharic](https://huggingface.co/rasyosef/roberta-base-amharic) 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.
 
+This model was presented in the paper **[The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://huggingface.co/papers/2605.24556)**.
+
+Official code repository: [https://github.com/rasyosef/amharic-neural-ir](https://github.com/rasyosef/amharic-neural-ir)
+
 ## Model Details
 
 ### Model Description
 - **Model Type:** Cross Encoder
-- **Base model:** [rasyosef/roberta-base-amharic](https://huggingface.co/rasyosef/roberta-base-amharic) <!-- at revision b1a3d2c267262e2b82c83be9d4e59db762a5e931 -->
+- **Base model:** [rasyosef/roberta-base-amharic](https://huggingface.co/rasyosef/roberta-base-amharic)
 - **Maximum Sequence Length:** 510 tokens
 - **Number of Output Labels:** 1 label
-<!-- - **Training Dataset:** Unknown -->
 - **Language:** am
 - **License:** mit
 
@@ -91,33 +94,10 @@ ranks = model.rank(
         'የቻይናው ፕሬዝዳንት ዚ ጂንፒንግ ከትራምፕ ጋር ባደረጉት ጉባኤ ትኩረታቸው በሁለቱ ሀገራት መካከል ለወራት ከተፈጠረ ውጥረት እና የንግድ ጦርነት በኋላ የተረገጋጋ ግንኙነትን ማስቀጠል ነበር። ከፑቲን ጋር ደግሞ ዢ ለሁለቱ አገራት ስልታዊም ሆነ ኢኮኖሚያዊ ጠቀሜታ ረጅም ጊዜ የዘለቀውን አጋርነትን ይበልጥ ማጠናከር ላይ ነበር ትኩረታቸው።',
     ]
 )
-# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+print(ranks)
+# [{'corpus_id': 0, 'score': np.float32(0.9555243)}, {'corpus_id': 1, 'score': np.float32(0.0012893651)}]
 ```
 
-<!--
-### 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
@@ -137,18 +117,6 @@ You can finetune this model on your own dataset.
 | mrr@10      | 0.830     |
 | **ndcg@10** | **0.856** |
 
-<!--
-## 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
 
 <details>
@@ -193,125 +161,6 @@ You can finetune this model on your own dataset.
 - `dataloader_num_workers`: 2
 - `load_best_model_at_end`: True
 
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: epoch
-- `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`: 4e-05
-- `weight_decay`: 0.1
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 4
-- `max_steps`: -1
-- `lr_scheduler_type`: cosine
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.05
-- `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`: True
-- `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`: 2
-- `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}
-- `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
-- `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 | amh-passage-retrieval-dev_ndcg@10 |
 |:-------:|:---------:|:-------------:|:---------------------------------:|
@@ -336,20 +185,11 @@ You can finetune this model on your own dataset.
 
 ## Citation
 
-<!--
-## 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.*
--->
+```bibtex
+@inproceedings{alemneh2026amharicir,
+  title     = {The Multilingual Curse at the Retrieval Layer: Evidence from Amharic},
+  author    = {Alemneh, Yosef Worku and Mekonnen, Kidist Amde and de Rijke, Maarten},
+  booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM), ACL 2026},
+  year      = {2026},
+}
+```