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") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 288
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 sha256:7a8c7e69826430aea25a097ecb6fcc0e5e5abb84bb46971842e94a618d067577
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -286,3 +286,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 286 |
285.0,0.33900494641745554,0.7187375547590601,0.1482919454574585,14.4269,178.347,5.615,0.25340069957248346,187530
|
| 287 |
286.0,0.34165383757785633,0.7192250372578242,0.14837491512298584,14.4262,178.356,5.615,0.2541780023319083,188188
|
| 288 |
287.0,0.34010605171603087,0.7194144301150227,0.1483272761106491,14.3252,179.614,5.654,0.2568985619898951,188846
|
|
|
|
|
|
| 286 |
285.0,0.33900494641745554,0.7187375547590601,0.1482919454574585,14.4269,178.347,5.615,0.25340069957248346,187530
|
| 287 |
286.0,0.34165383757785633,0.7192250372578242,0.14837491512298584,14.4262,178.356,5.615,0.2541780023319083,188188
|
| 288 |
287.0,0.34010605171603087,0.7194144301150227,0.1483272761106491,14.3252,179.614,5.654,0.2568985619898951,188846
|
| 289 |
+
288.0,0.3400609278709284,0.7199086712661935,0.1483636051416397,14.5189,177.218,5.579,0.2522347454333463,189504
|