Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v2_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v2_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_sst2") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Hartunka/tiny_bert_rand_5_v2_sst2: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_sst2/resolve/main/train_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v2_sst2/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_sst2/resolve/main/train_results.json
239 Bytes
| { | |
| "epoch": 6.0, | |
| "total_flos": 1.0596790573590528e+16, | |
| "train_loss": 0.20675967678879248, | |
| "train_runtime": 217.3543, | |
| "train_samples": 67349, | |
| "train_samples_per_second": 15492.91, | |
| "train_steps_per_second": 60.73 | |
| } |