Instructions to use FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/trainer_state.json
- Command line
-
hf download hf://FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/FKBaffour/fine-tuned_bert_base_model_for_sentiment_analysis/resolve/main/trainer_state.json
1.21 kB
| { | |
| "best_metric": 0.6177173852920532, | |
| "best_model_checkpoint": "test_trainer/checkpoint-2000", | |
| "epoch": 2.0, | |
| "global_step": 2000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.5, | |
| "learning_rate": 4.5e-05, | |
| "loss": 0.75, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 4e-05, | |
| "loss": 0.6967, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 0.6491263508796692, | |
| "eval_runtime": 60.4692, | |
| "eval_samples_per_second": 33.075, | |
| "eval_steps_per_second": 4.134, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 1.5, | |
| "learning_rate": 3.5e-05, | |
| "loss": 0.5624, | |
| "step": 1500 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "learning_rate": 3e-05, | |
| "loss": 0.5723, | |
| "step": 2000 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_loss": 0.6177173852920532, | |
| "eval_runtime": 60.461, | |
| "eval_samples_per_second": 33.079, | |
| "eval_steps_per_second": 4.135, | |
| "step": 2000 | |
| } | |
| ], | |
| "max_steps": 5000, | |
| "num_train_epochs": 5, | |
| "total_flos": 4209288456812544.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |