Text Classification
Transformers
PyTorch
Safetensors
OpenVINO
xlm-roberta
Generated from Trainer
language-identification
Eval Results (legacy)
text-embeddings-inference
Instructions to use juliensimon/xlm-v-base-language-id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliensimon/xlm-v-base-language-id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juliensimon/xlm-v-base-language-id")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("juliensimon/xlm-v-base-language-id") model = AutoModelForSequenceClassification.from_pretrained("juliensimon/xlm-v-base-language-id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
43f6bb2
1
Parent(s): c953a57
Librarian Bot: Add base_model information to model (#5)
Browse files- Librarian Bot: Add base_model information to model (4571ced2106e33b744ec5def5a5e99d9aa933c64)
Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>
README.md
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- fleurs
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metrics:
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- accuracy
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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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name: Text Classification
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type: text-classification
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dataset:
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name: fleurs
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type: fleurs
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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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---
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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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- 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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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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