End of training
Browse files- README.md +44 -179
- all_results.json +21 -0
- config.json +1 -1
- eval_results.json +11 -0
- train_results.json +8 -0
- trainer_state.json +305 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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library_name: transformers
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base_model: dccuchile/bert-base-spanish-wwm-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: Bert_v12
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Bert_v12
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Accuracy: 0.9063
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- F1: 0.9070
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- Precision: 0.9093
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- Recall: 0.9082
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- Loss: 0.4002
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 20
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### Training results
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.5.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 9.0,
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"eval_accuracy": 0.9063492063492063,
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"eval_f1": 0.9069930228990634,
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"eval_loss": 0.4001522362232208,
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"eval_precision": 0.909308614451706,
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"eval_recall": 0.9081607205793748,
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"eval_runtime": 114.4852,
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"eval_samples_per_second": 33.017,
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"eval_steps_per_second": 1.039,
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"total_flos": 2.08932565653504e+16,
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"train_eval_accuracy": 0.9653061224489796,
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"train_eval_f1": 0.9649036069253767,
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"train_eval_loss": 0.13657130300998688,
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"train_eval_precision": 0.966025675321776,
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"train_eval_recall": 0.9649842543919382,
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"train_loss": 0.7482529300806411,
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"train_runtime": 11065.7342,
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"train_samples_per_second": 15.941,
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"train_steps_per_second": 0.499
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}
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"BertForSequenceClassification"
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{
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"_name_or_path": "dccuchile/bert-base-spanish-wwm-uncased",
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"architectures": [
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"BertForSequenceClassification"
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eval_results.json
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{
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"epoch": 9.0,
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"eval_accuracy": 0.9063492063492063,
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"eval_f1": 0.9069930228990634,
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"eval_loss": 0.4001522362232208,
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"eval_precision": 0.909308614451706,
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"eval_recall": 0.9081607205793748,
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"eval_runtime": 114.4852,
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"eval_samples_per_second": 33.017,
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"eval_steps_per_second": 1.039
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}
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train_results.json
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{
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"epoch": 9.0,
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"total_flos": 2.08932565653504e+16,
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"train_loss": 0.7482529300806411,
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"train_runtime": 11065.7342,
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"train_samples_per_second": 15.941,
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"train_steps_per_second": 0.499
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}
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trainer_state.json
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