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 302
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 sha256:6f449fb7472c976588c04ded6a0f5225a75deda470afc20999ea9da07ec7b378
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -300,3 +300,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 300 |
299.0,0.34205628065586946,0.7201190771520715,0.14823143184185028,14.3905,178.799,5.629,0.2541780023319083,196742
|
| 301 |
300.0,0.342344236697746,0.7204050230803594,0.14818550646305084,14.2949,179.994,5.666,0.25340069957248346,197400
|
| 302 |
301.0,0.3419657792391,0.7197772141827042,0.14813284575939178,14.361,179.166,5.64,0.25573260785075785,198058
|
|
|
|
|
|
| 300 |
299.0,0.34205628065586946,0.7201190771520715,0.14823143184185028,14.3905,178.799,5.629,0.2541780023319083,196742
|
| 301 |
300.0,0.342344236697746,0.7204050230803594,0.14818550646305084,14.2949,179.994,5.666,0.25340069957248346,197400
|
| 302 |
301.0,0.3419657792391,0.7197772141827042,0.14813284575939178,14.361,179.166,5.64,0.25573260785075785,198058
|
| 303 |
+
302.0,0.34109198554655623,0.7200557796703023,0.14807672798633575,14.3026,179.898,5.663,0.25573260785075785,198716
|