Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,444 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:40000
+- loss:TripletLoss
+base_model: sentence-transformers/paraphrase-xlm-r-multilingual-v1
+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።
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+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]
+```
+
+<!--
+### 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
+
+#### 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**                 |
+
+<!--
+## 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
+
+#### 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}
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "sentence-transformers/paraphrase-xlm-r-multilingual-v1",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 1112197096

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+{
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tokenizer.json

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+size 17083053

tokenizer_config.json

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+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}