Instructions to use LexFerrinson/FirulaiModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LexFerrinson/FirulaiModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LexFerrinson/FirulaiModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LexFerrinson/FirulaiModel") model = AutoModelForTokenClassification.from_pretrained("LexFerrinson/FirulaiModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
e6dd533
1
Parent(s): 344f3d0
Training in progress epoch 0
Browse files- README.md +6 -9
- config.json +1 -1
- special_tokens_map.json +35 -5
- tf_model.h5 +1 -1
- tokenizer.json +1 -6
- tokenizer_config.json +4 -0
README.md
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_keras_callback
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model-index:
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# LexFerrinson/FirulaiModel
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This model
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Train Precision: 0.0
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- Train Recall: 0.0
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- Train F1: 0.0
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- Train Accuracy: 0.9082
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- Epoch:
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps':
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 0.
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| 0.3269 | 0.2917 | 0.0 | 0.0 | 0.0 | 0.9082 | 1 |
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### Framework versions
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---
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tags:
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- generated_from_keras_callback
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model-index:
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# LexFerrinson/FirulaiModel
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.2941
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- Validation Loss: 0.2539
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- Train Precision: 0.0
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- Train Recall: 0.0
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- Train F1: 0.0
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- Train Accuracy: 0.9082
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- Epoch: 0
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 0.2941 | 0.2539 | 0.0 | 0.0 | 0.0 | 0.9082 | 0 |
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### Framework versions
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config.json
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{
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"_name_or_path": "
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"activation": "gelu",
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"architectures": [
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"DistilBertForTokenClassification"
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{
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"_name_or_path": "LexFerrinson/FirulaiModel",
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"activation": "gelu",
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"architectures": [
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"DistilBertForTokenClassification"
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special_tokens_map.json
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{
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"cls_token":
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}
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 265587984
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d3beb392190d6a1a66b1658e638f207225bad6431a8b7ee89ca1f3755a1c947
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size 265587984
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tokenizer.json
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{
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"version": "1.0",
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"truncation":
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"direction": "Right",
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"max_length": 512,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": null,
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"added_tokens": [
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"version": "1.0",
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"truncation": null,
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"padding": null,
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"added_tokens": [
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tokenizer_config.json
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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