Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use KCourtney/distilbert-base-uncased-distilled-clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KCourtney/distilbert-base-uncased-distilled-clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KCourtney/distilbert-base-uncased-distilled-clinc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc") model = AutoModelForSequenceClassification.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,004 Bytes
71b5009 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0471
- Accuracy: 0.9319
## 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 0.2645 | 0.6329 |
| 0.3989 | 2.0 | 636 | 0.1324 | 0.8510 |
| 0.3989 | 3.0 | 954 | 0.0897 | 0.8984 |
| 0.1467 | 4.0 | 1272 | 0.0702 | 0.9084 |
| 0.0939 | 5.0 | 1590 | 0.0591 | 0.9245 |
| 0.0939 | 6.0 | 1908 | 0.0531 | 0.9265 |
| 0.0749 | 7.0 | 2226 | 0.0499 | 0.9306 |
| 0.0665 | 8.0 | 2544 | 0.0477 | 0.9316 |
| 0.0665 | 9.0 | 2862 | 0.0471 | 0.9319 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|