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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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- language-identification |
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- openvino |
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datasets: |
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- fleurs |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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base_model: facebook/xlm-v-base |
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model-index: |
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- name: xlm-v-base-language-id |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: fleurs |
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type: fleurs |
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config: all |
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split: validation |
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args: all |
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metrics: |
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- type: accuracy |
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value: 0.9930337861372344 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-v-base-language-id |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the [google/fleurs](https://huggingface.co/datasets/google/fleurs) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0241 |
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- Accuracy: 0.9930 |
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# Usage |
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The simplest way to use the model is with a text classification pipeline: |
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``` |
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from transformers import pipeline |
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model_id = "juliensimon/xlm-v-base-language-id" |
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p = pipeline("text-classification", model=model_id) |
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p("Hello world") |
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# [{'label': 'English', 'score': 0.9802148342132568}] |
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``` |
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The model is also compatible with [Optimum Intel](https://github.com/huggingface/optimum-intel). |
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For example, you can optimize it with Intel OpenVINO and enjoy a 2x inference speedup (or more). |
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``` |
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from optimum.intel.openvino import OVModelForSequenceClassification |
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from transformers import AutoTokenizer, pipeline |
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model_id = "juliensimon/xlm-v-base-language-id" |
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ov_model = OVModelForSequenceClassification.from_pretrained(model_id) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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p = pipeline("text-classification", model=ov_model, tokenizer=tokenizer) |
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p("Hello world") |
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# [{'label': 'English', 'score': 0.9802149534225464}] |
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``` |
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An OpenVINO version of the model is available in the repository. |
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## Intended uses & limitations |
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The model can accurately detect 102 languages. You can find the list on the [dataset](https://huggingface.co/datasets/google/fleurs) page. |
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## Training and evaluation data |
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The model has been trained and evaluated on the complete google/fleurs training and validation sets. |
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## Training procedure |
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The training script is included in the repository. The model has been trained on an p3dn.24xlarge instance on AWS (8 NVIDIA V100 GPUs). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6368 | 1.0 | 531 | 0.4593 | 0.9689 | |
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| 0.059 | 2.0 | 1062 | 0.0412 | 0.9899 | |
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| 0.0311 | 3.0 | 1593 | 0.0275 | 0.9918 | |
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| 0.0255 | 4.0 | 2124 | 0.0243 | 0.9928 | |
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| 0.017 | 5.0 | 2655 | 0.0241 | 0.9930 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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