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 510
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:b47522d4006442f2629c81d2d3cc9e1f7013d2d49e8df8efd1356b45bef99dd4
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -508,3 +508,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 508 |
507.0,0.3495568712693217,0.7236515347173935,0.14673784375190735,14.4272,178.343,5.614,0.26078507578701904,333606
|
| 509 |
508.0,0.3480373364535791,0.7230340988169798,0.14675956964492798,14.3504,179.298,5.644,0.26000777302759426,334264
|
| 510 |
509.0,0.3498300863272387,0.7237972295318008,0.14675353467464447,14.5388,176.975,5.571,0.26039642440730665,334922
|
|
|
|
|
|
| 508 |
507.0,0.3495568712693217,0.7236515347173935,0.14673784375190735,14.4272,178.343,5.614,0.26078507578701904,333606
|
| 509 |
508.0,0.3480373364535791,0.7230340988169798,0.14675956964492798,14.3504,179.298,5.644,0.26000777302759426,334264
|
| 510 |
509.0,0.3498300863272387,0.7237972295318008,0.14675353467464447,14.5388,176.975,5.571,0.26039642440730665,334922
|
| 511 |
+
510.0,0.34916864669774245,0.7234063695533363,0.1467435359954834,14.3517,179.281,5.644,0.26039642440730665,335580
|