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 570
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 sha256:06213b515d1f4cf290a76e177da23c1b88ba533ad6aaf36b78e26033b9e54266
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -568,3 +568,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 568 |
567.0,0.3501163978902668,0.723851030110935,0.1466645449399948,14.4293,178.318,5.614,0.2592304702681695,373086
|
| 569 |
568.0,0.3506598456425815,0.7242745369911855,0.14666372537612915,14.4993,177.457,5.586,0.26000777302759426,373744
|
| 570 |
569.0,0.35024395251583357,0.7237821212421868,0.14658403396606445,14.5289,177.095,5.575,0.26039642440730665,374402
|
|
|
|
|
|
| 568 |
567.0,0.3501163978902668,0.723851030110935,0.1466645449399948,14.4293,178.318,5.614,0.2592304702681695,373086
|
| 569 |
568.0,0.3506598456425815,0.7242745369911855,0.14666372537612915,14.4993,177.457,5.586,0.26000777302759426,373744
|
| 570 |
569.0,0.35024395251583357,0.7237821212421868,0.14658403396606445,14.5289,177.095,5.575,0.26039642440730665,374402
|
| 571 |
+
570.0,0.3502547591771642,0.7239634993056934,0.14664824306964874,14.6143,176.061,5.543,0.26117372716673143,375060
|