sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:40000 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:sentence-transformers/paraphrase-xlm-r-multilingual-v1 base_model:finetune:sentence-transformers/paraphrase-xlm-r-multilingual-v1 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-xlm-r-multilingual-v1
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:40000
- loss:TripletLoss
widget:
- source_sentence: ልጆች በመዝናኛ መናፈሻ ውስጥ ፊኛ ይጋልባሉ።
  sentences:
  - በጉዞ ላይ ያሉ ሰዎች
  - ፓርኩ ባዶ ነበር
  - መነጽር ያለባት ሴት ማይክሮፎን ውስጥ እየዘፈነች.
- source_sentence: በላዩ ላይ ካለው ህንፃ አጠገብ ያሉት ሦስት ሰዎች ቆመው ነበር.
  sentences:
  - አንዳንድ ልጆች በመዝናኛ መናፈሻ ቦታ ፊኛ ግልቢያ ላይ ናቸው።
  - ሰዎች ውጭ ናቸው.
  - ሰዎች ቦውሊንግ ናቸው።
- source_sentence: ሰው እየሮጠ ነው።
  sentences:
  - እግሩ ላይ ጫማ ሳይኖረው የሚሮጥ ሰው።
  - ፎቶ ለመነሳት በትልቅ ድንጋይ ላይ የተቀመጠ ሰው።
  - ጎዳና ላይ የሚሮጥ ወጣት።
- source_sentence: ሐምራዊ ልብስ ከነጭ ሐምራዊ ልብስ ጋር አንድ ሰው እጁን በሌላ ሰው በግንባታው ላይ ከፍ አደረገ.
  sentences:
  - ነጭ ልብስ የለበሰ ሰው እጁን በሌላው ሰው ግንባሩ ላይ አደረገ።
  - ባለብዙ ልብስ እና ቀይ ብርጭቆ ያላቸው ወጣት ሴት አንድ ወጣት ሴት እየተመለከተች እያለ የተወሰነ የእጅ ቦታን ያስተካክላል.
  - አንድ ቄስ በእምነቱ ላይ ብቻውን እያሰላሰለ ነው።
- source_sentence: ሰዎች ተቀምጠዋል
  sentences:
  - ሁለት ሰዎች አንድ ልብ በምድር ውስጥ ከተቀመጠባቸው ጥቂት እግሮች ጥቂት መሬት ተቀምጠዋል.
  - ሰዎች እየተራመዱ ሲሆን አንድ ወንድ ጭንቅላቱን ወደ ግራ ዞሯል.
  - አንድ ሰው በበረዶ መንሸራተቻ መናፈሻ ውስጥ ካለው መወጣጫ ላይ ብስክሌቱን እየዘለለ ነው።
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-xlm-r-multilingual-v1
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: amharic xlmr nli dev
      type: amharic-xlmr-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.815
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: amharic xlmr finetuned dev
      type: amharic-xlmr-finetuned-dev
    metrics:
    - type: cosine_accuracy
      value: 0.8575
      name: Cosine Accuracy
---

# SentenceTransformer based on sentence-transformers/paraphrase-xlm-r-multilingual-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) on the csv dataset. 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:** [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) <!-- at revision 000e995b707ecea1b901208915ff3533783ec13d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("abdulmunimjemal/amharic-xlmr-finetuned")
# Run inference
sentences = [
    'ሰዎች ተቀምጠዋል',
    'ሁለት ሰዎች አንድ ልብ በምድር ውስጥ ከተቀመጠባቸው ጥቂት እግሮች ጥቂት መሬት ተቀምጠዋል.',
    'ሰዎች እየተራመዱ ሲሆን አንድ ወንድ ጭንቅላቱን ወደ ግራ ዞሯል.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Triplet

* Datasets: `amharic-xlmr-nli-dev` and `amharic-xlmr-finetuned-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | amharic-xlmr-nli-dev | amharic-xlmr-finetuned-dev |
|:--------------------|:---------------------|:---------------------------|
| **cosine_accuracy** | **0.815**            | **0.8575**                 |

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## 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.*
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### Recommendations

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 40,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                         | positive                                                                           | negative                                                                           |
  |:--------|:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                         | string                                                                             | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 6.5 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 24.81 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 26.34 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | anchor                   | positive                                                                    | negative                                                             |
  |:-------------------------|:----------------------------------------------------------------------------|:---------------------------------------------------------------------|
  | <code>ሰውየው ውጭ ነው.</code> | <code>በቤቶች በተከበበችው የኪሳ ፓርክ ውስጥ ረዥም ፀጉር ያለው አንድ ሰው የመንሸራተቻ ሰሌዳ ነው.</code>    | <code>በወንድ ወንበር ላይ በወንድ ወንበር ላይ በመዝገቢያ ወረቀት ላይ በመዝጋት ላይ ይተኛል.</code> |
  | <code>ሰውየው ውጭ ነው።</code> | <code>ረጅም ፀጉር ያለው ሰው በቤቶች በተከበበ የበረዶ መንሸራተቻ ፓርክ ውስጥ በስኬትቦርዲንግ ላይ ነው።</code> | <code>በቀን ውስጥ አንድ ሰው በአንድ ክፍል ውስጥ የእጅ መቆንጠጫ ይሠራል.</code>             |
  | <code>ሰውየው ውጭ ነው.</code> | <code>በቤቶች በተከበበችው የኪሳ ፓርክ ውስጥ ረዥም ፀጉር ያለው አንድ ሰው የመንሸራተቻ ሰሌዳ ነው.</code>    | <code>ባለሙያ የለበሰ ሰው ከኮንሶል ፊት ለፊት ተቀምጧል።</code>                        |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 40,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 13.91 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.28 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 21.07 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
  | anchor                                           | positive                             | negative                                |
  |:-------------------------------------------------|:-------------------------------------|:----------------------------------------|
  | <code>ቡናማ ውሻ እየሮጠ እና እየተመለከተ ነው.</code>          | <code>ቡናማ ውሻ ወደ ሰማይ እያየ ይሮጣል.</code> | <code>ከዛፉ ስር አንድ ቡናማ ውሻ ይዞ ይገኛል.</code> |
  | <code>በወርቃማ ቀለም ያለው ቀለም ያለው ቆዳዎች በሣር ውስጥ.</code> | <code>ውሻ በሳሩ ውስጥ ነው.</code>          | <code>ውሻ ውጭ እየተጣደፈ ነው.</code>           |
  | <code>ቡናማ ውሻ ከቤት ውጭ እየተጫወተ ነው.</code>            | <code>አንድ እንስሳ ውጭ ነው.</code>         | <code>ቡናማ ውሻ በኩሽና ውስጥ እየበላ ነው.</code>   |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### 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`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `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`: 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`: 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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | amharic-xlmr-nli-dev_cosine_accuracy | amharic-xlmr-finetuned-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:------------------------------------:|:------------------------------------------:|
| 0      | 0    | -             | -               | 0.6432                               | -                                          |
| 0.05   | 100  | 4.6673        | 4.3076          | 0.8492                               | -                                          |
| 0.1    | 200  | 4.1006        | 3.6344          | 0.821                                | -                                          |
| 0.15   | 300  | 3.843         | 4.1666          | 0.7652                               | -                                          |
| 0.2    | 400  | 4.0508        | 3.8094          | 0.815                                | -                                          |
| 0.25   | 500  | 3.9858        | -               | -                                    | 0.8237                                     |
| 0.2    | 100  | 4.15          | -               | -                                    | -                                          |
| 0.4    | 200  | 4.1811        | -               | -                                    | -                                          |
| 0.6    | 300  | 4.3359        | -               | -                                    | -                                          |
| 0.8    | 400  | 4.382         | -               | -                                    | -                                          |
| 1.0    | 500  | 3.6309        | 3.5175          | -                                    | 0.858                                      |
| 1.198  | 600  | 4.1283        | -               | -                                    | -                                          |
| 1.3980 | 700  | 4.0372        | -               | -                                    | -                                          |
| 1.5980 | 800  | 4.2113        | -               | -                                    | -                                          |
| 1.798  | 900  | 4.059         | -               | -                                    | -                                          |
| 1.998  | 1000 | 3.4594        | 3.5366          | -                                    | 0.8565                                     |
| 2.196  | 1100 | 4.0407        | -               | -                                    | -                                          |
| 2.396  | 1200 | 3.9531        | -               | -                                    | -                                          |
| 2.596  | 1300 | 4.1321        | -               | -                                    | -                                          |
| 2.7960 | 1400 | 3.9537        | -               | -                                    | -                                          |
| 2.996  | 1500 | 3.4291        | 3.5476          | -                                    | 0.8575                                     |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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