Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_log.csv from spencercdz/xlm-roberta-sentiment-requests: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
-
https://huggingface.co/spencercdz/xlm-roberta-sentiment-requests/resolve/f6e2dfcd3beffd5f2b51af61fe8b98775d3a0989/training_log.csv
- Command line
-
hf download hf://spencercdz/xlm-roberta-sentiment-requests@f6e2dfcd3beffd5f2b51af61fe8b98775d3a0989/training_log.csv
-
curl -L -o training_log.csv https://huggingface.co/spencercdz/xlm-roberta-sentiment-requests/resolve/f6e2dfcd3beffd5f2b51af61fe8b98775d3a0989/training_log.csv
1.01 kB
| epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second,eval_steps_per_second,eval_subset_accuracy,step | |
| 1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658 | |
| 2.0,0.09064480010203674,0.544629877304181,0.22911879420280457,14.4231,178.394,5.616,0.11232024873688301,1316 | |
| 3.0,0.10309514614135397,0.5682428955343358,0.21433667838573456,16.6207,154.807,4.873,0.12786630392537893,1974 | |
| 4.0,0.115960350269004,0.5877596855699045,0.20582592487335205,14.3765,178.973,5.634,0.1333074232413525,2632 | |
| 5.0,0.12550720862659767,0.6022160664819944,0.1996934562921524,14.2268,180.855,5.693,0.1379712397979013,3290 | |
| 6.0,0.13109005516624317,0.6116456809809204,0.19493445754051208,14.4708,177.806,5.597,0.14380101049358726,3948 | |
| 7.0,0.1371925209709484,0.6188629785374912,0.19109617173671722,14.6334,175.831,5.535,0.14380101049358726,4606 | |
| 8.0,0.14152951052414994,0.6225964149918523,0.18794220685958862,14.3092,179.814,5.661,0.14457831325301204,5264 | |