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
Training in progress, epoch 238
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:d0eb643b0a0fbd7b5ac2844009478cb8eb62c476a73c6238b435770834ca0e11
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -236,3 +236,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 236 |
235.0,0.3356625607235986,0.7175191943364244,0.14904944598674774,14.2866,180.099,5.67,0.2526233968130587,154630
|
| 237 |
236.0,0.3374719033850877,0.7178720860729229,0.1490362584590912,14.5985,176.251,5.549,0.2518460940536339,155288
|
| 238 |
237.0,0.33692668083185146,0.7179615039393638,0.1490037590265274,14.2898,180.058,5.668,0.25340069957248346,155946
|
|
|
|
|
|
| 236 |
235.0,0.3356625607235986,0.7175191943364244,0.14904944598674774,14.2866,180.099,5.67,0.2526233968130587,154630
|
| 237 |
236.0,0.3374719033850877,0.7178720860729229,0.1490362584590912,14.5985,176.251,5.549,0.2518460940536339,155288
|
| 238 |
237.0,0.33692668083185146,0.7179615039393638,0.1490037590265274,14.2898,180.058,5.668,0.25340069957248346,155946
|
| 239 |
+
238.0,0.3366017888109203,0.7174097135740971,0.14901141822338104,14.3412,179.413,5.648,0.2526233968130587,156604
|