Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use whispAI/bert-claimcoherence-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whispAI/bert-claimcoherence-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whispAI/bert-claimcoherence-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whispAI/bert-claimcoherence-mini") model = AutoModelForSequenceClassification.from_pretrained("whispAI/bert-claimcoherence-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - text-classification | |
| language: | |
| - en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - lucafrost/autotrain-data-claimcoherence-lf | |
| co2_eq_emissions: | |
| emissions: 0.5905299701991715 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Binary Classification | |
| - Model ID: 39443102994 | |
| - CO2 Emissions (in grams): 0.5905 | |
| ## Validation Metrics | |
| - Loss: 0.396 | |
| - Accuracy: 0.820 | |
| - Precision: 0.913 | |
| - Recall: 0.750 | |
| - AUC: 0.907 | |
| - F1: 0.824 | |
| ## Usage | |
| You can use cURL to access this model: | |
| ``` | |
| $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lucafrost/autotrain-claimcoherence-lf-39443102994 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("lucafrost/autotrain-claimcoherence-lf-39443102994", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("lucafrost/autotrain-claimcoherence-lf-39443102994", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |