Instructions to use AhmadFareedKhan/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AhmadFareedKhan/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AhmadFareedKhan/model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AhmadFareedKhan/model") model = AutoModelForMaskedLM.from_pretrained("AhmadFareedKhan/model") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: Twitter/twhin-bert-large | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: model | |
| 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. --> | |
| # model | |
| This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.8996 | |
| ## 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: 1e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 300 | 2.1878 | | |
| | 2.4077 | 2.0 | 600 | 2.0959 | | |
| | 2.4077 | 3.0 | 900 | 2.1126 | | |
| | 2.2053 | 4.0 | 1200 | 2.0066 | | |
| | 2.0736 | 5.0 | 1500 | 1.9590 | | |
| | 2.0736 | 6.0 | 1800 | 1.9668 | | |
| | 2.0221 | 7.0 | 2100 | 1.9509 | | |
| | 2.0221 | 8.0 | 2400 | 1.9274 | | |
| | 1.9679 | 9.0 | 2700 | 1.8871 | | |
| | 1.9687 | 10.0 | 3000 | 1.8996 | | |
| ### Framework versions | |
| - Transformers 4.32.1 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.13.3 | |