Instructions to use kiddothe2b/longformer-replicated-pos-encodings-4096-2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiddothe2b/longformer-replicated-pos-encodings-4096-2L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kiddothe2b/longformer-replicated-pos-encodings-4096-2L")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/longformer-replicated-pos-encodings-4096-2L") model = AutoModelForMaskedLM.from_pretrained("kiddothe2b/longformer-replicated-pos-encodings-4096-2L") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- generated_from_trainer
datasets:
- c4
metrics:
- accuracy
model-index:
- name: longformer-replicated-pos-encodings-4096-2L
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: c4 en
type: c4
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.6366230517315061
longformer-replicated-pos-encodings-4096-2L
This model is a fine-tuned version of data/models/longformer-replicated-pos-encodings-4096 on the c4 en dataset. It achieves the following results on the evaluation set:
- Loss: 1.9437
- Accuracy: 0.6366
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 1.0
- training_steps: 6400
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1052 | 1.0 | 6400 | 1.9433 | 0.6366 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.12.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1