Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use coderSounak/finetuning-hs-model-bert-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use coderSounak/finetuning-hs-model-bert-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="coderSounak/finetuning-hs-model-bert-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("coderSounak/finetuning-hs-model-bert-multilingual") model = AutoModelForSequenceClassification.from_pretrained("coderSounak/finetuning-hs-model-bert-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from coderSounak/finetuning-hs-model-bert-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/coderSounak/finetuning-hs-model-bert-multilingual/resolve/main/README.md
- Command line
-
hf download hf://coderSounak/finetuning-hs-model-bert-multilingual/README.md
-
curl -L -o README.md https://huggingface.co/coderSounak/finetuning-hs-model-bert-multilingual/resolve/main/README.md
1.31 kB
metadata
license: cc-by-nc-3.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-hs-model-bert-multilingual
results: []
finetuning-hs-model-bert-multilingual
This model is a fine-tuned version of QCRI/bert-base-multilingual-cased-pos-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3158
- Accuracy: 0.9575
- F1: 0.0
- Precision: 0.0
- Recall: 0.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2