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
|
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
| license: cc-by-nc-3.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: finetuning-hs-model-bert-multilingual | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # finetuning-hs-model-bert-multilingual | |
| This model is a fine-tuned version of [QCRI/bert-base-multilingual-cased-pos-english](https://huggingface.co/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 | |