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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tweet_eval |
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model-index: |
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- name: MND_TweetEvalBert_model |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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metrics: |
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- accuracy |
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widget: |
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- text: 'I loved Barbie and Oppenheimer' |
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example_title: Barbenheimer |
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--- |
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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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# MND_TweetEvalBert_model |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7241 |
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## Model description |
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This is how to use the model with the transformer library to do a text classification task. |
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This model was trained and built for sentiment analysis with a text classification model architecture. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") |
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model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") |
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# We can now use the model in the pipeline. |
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classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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# Get some text to fool around with for a basic test. |
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text = "I loved Oppenheimer and Barbie " |
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classifier(text) # Let's see if the model works on our example text. |
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``` |
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``` |
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[{'label': 'JOY', 'score': 0.9845513701438904}] |
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``` |
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## Training Evalutation Results |
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```python |
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{'eval_loss': 0.7240552306175232, |
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'eval_runtime': 3.7803, |
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'eval_samples_per_second': 375.896, |
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'eval_steps_per_second': 23.543, |
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'epoch': 5.0} |
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``` |
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## Overall Model Evaluation Results |
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```python |
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{'accuracy': {'confidence_interval': (0.783, 0.832), |
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'standard_error': 0.01241992329458207, |
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'score': 0.808}, |
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'total_time_in_seconds': 150.93268656500004, |
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'samples_per_second': 6.625470087086432, |
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'latency_in_seconds': 0.15093268656500003} |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: linear |
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- num_epochs: 5 |
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### Training results |
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```python |
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{'training_loss'=0.3821827131159165} |
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{'train_runtime': 174.1546, 'train_samples_per_second': 93.509, |
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'train_steps_per_second': 5.857, 'total_flos': 351397804992312.0, |
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'train_loss': 0.3821827131159165, 'epoch': 5.0} |
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``` |
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``` |
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Step: 500 |
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{training loss: 0.607100} |
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Step: 1000 |
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{training loss: 0.169000} |
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``` |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
