This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: FacebookAI/xlm-roberta-base
Maximum Sequence Length: 64 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 64, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

import json
from sentence_transformers import SentenceTransformer, util

# load trained model
model = SentenceTransformer("dice-research/amharic-property-retriever-mbert")

# input field you want to test
query = "book's ቋንቋ"


# load all candidate properties from dataset
properties = set()

with open("dice-research/amharic-property-retriever-base-xlmr", "r", encoding="utf-8") as f:
    for line in f:
        row = json.loads(line)
        properties.add(row["property_text"])

properties = list(properties)

# compute embeddings
query_emb = model.encode(query, convert_to_tensor=True, normalize_embeddings=True)
prop_emb = model.encode(properties, convert_to_tensor=True, normalize_embeddings=True)

# compute similarity
scores = util.cos_sim(query_emb, prop_emb)[0]

# get top 5 predictions
top_k = 5
top_results = scores.topk(top_k)

print("Input:", query)
print("\nTop 5 predictions:")

for idx, score in zip(top_results.indices, top_results.values):
    print(properties[idx], score.item())

Evaluation

Metrics

Information Retrieval

Dataset: dev-ir
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4677
cosine_accuracy@3	0.6344
cosine_accuracy@5	0.6935
cosine_accuracy@10	0.7903
cosine_precision@1	0.4677
cosine_precision@3	0.2115
cosine_precision@5	0.1387
cosine_precision@10	0.079
cosine_recall@1	0.4677
cosine_recall@3	0.6344
cosine_recall@5	0.6935
cosine_recall@10	0.7903
cosine_ndcg@10	0.6218
cosine_mrr@10	0.5685
cosine_map@100	0.5769

Training Details

Training Dataset

Size: 1,224 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 8.2 tokens
max: 16 tokens
min: 3 tokens
mean: 3.66 tokens
max: 8 tokens
Samples:
anchor positive
case's header title
airline's ንብረቶች assets
person's እናት mother
Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

Size: 186 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 186 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 8.21 tokens
max: 13 tokens
min: 3 tokens
mean: 3.62 tokens
max: 8 tokens
Samples:
anchor positive
soccer player's ዓመታት years
case's rowclass class
type's accessyear year
Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 4
warmup_ratio: 0.1
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
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
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: 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
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	dev-ir_cosine_ndcg@10
0.6410	25	3.6634	-	-
1.0	39	-	1.2992	0.1523
1.2821	50	3.3099	-	-
1.9231	75	3.3554	-	-
2.0	78	-	0.9091	0.4585
2.5641	100	2.6155	-	-
3.0	117	-	0.4628	0.6086
3.2051	125	2.0285	-	-
3.8462	150	1.9384	-	-
4.0	156	-	0.4143	0.6218

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.9.2
Sentence Transformers: 5.1.2
Transformers: 4.57.6
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.5.0
Tokenizers: 0.22.2

	anchor	positive
type	string	string
details	min: 5 tokens mean: 8.2 tokens max: 16 tokens	min: 3 tokens mean: 3.66 tokens max: 8 tokens

anchor	positive
`case's header`	`title`
`airline's ንብረቶች`	`assets`
`person's እናት`	`mother`

anchor	positive
`soccer player's ዓመታት`	`years`
`case's rowclass`	`class`
`type's accessyear`	`year`