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")# 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
Training in progress, epoch 29
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1109972056
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:731e58aaf6605d12935dd6b2e4a41b80fbaf3626ce823a01bd2ec37d347ed2a2
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -27,3 +27,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 27 |
26.0,0.2327108644169528,0.6726620574933722,0.1665038764476776,14.404,178.63,5.623,0.19665759813447337,17108
|
| 28 |
27.0,0.23581474059807764,0.6748676424789785,0.16596576571464539,14.3067,179.845,5.662,0.20326467158958414,17766
|
| 29 |
28.0,0.23677158413716617,0.6749598299901519,0.1655886024236679,14.4878,177.598,5.591,0.20015546055188496,18424
|
|
|
|
|
|
| 27 |
26.0,0.2327108644169528,0.6726620574933722,0.1665038764476776,14.404,178.63,5.623,0.19665759813447337,17108
|
| 28 |
27.0,0.23581474059807764,0.6748676424789785,0.16596576571464539,14.3067,179.845,5.662,0.20326467158958414,17766
|
| 29 |
28.0,0.23677158413716617,0.6749598299901519,0.1655886024236679,14.4878,177.598,5.591,0.20015546055188496,18424
|
| 30 |
+
29.0,0.237100621510197,0.6751843772722551,0.16502319276332855,14.3821,178.903,5.632,0.20171006607073455,19082
|