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 195
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 sha256:3b5904e80a23fb5eba55181b1ddd21e523b00f73f88ab11f74a842a63d5a96b5
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -193,3 +193,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 193 |
192.0,0.33243083580374094,0.7160001991932673,0.14993004500865936,14.423,178.396,5.616,0.2495141857753595,126336
|
| 194 |
193.0,0.33035332270826173,0.7152535618212613,0.14996753633022308,14.4188,178.447,5.618,0.2483482316362223,126994
|
| 195 |
194.0,0.33152080145195545,0.7161480445532191,0.1498352736234665,14.279,180.195,5.673,0.2518460940536339,127652
|
|
|
|
|
|
| 193 |
192.0,0.33243083580374094,0.7160001991932673,0.14993004500865936,14.423,178.396,5.616,0.2495141857753595,126336
|
| 194 |
193.0,0.33035332270826173,0.7152535618212613,0.14996753633022308,14.4188,178.447,5.618,0.2483482316362223,126994
|
| 195 |
194.0,0.33152080145195545,0.7161480445532191,0.1498352736234665,14.279,180.195,5.673,0.2518460940536339,127652
|
| 196 |
+
195.0,0.33043089849103635,0.7162911525203577,0.14982862770557404,14.4205,178.427,5.617,0.2522347454333463,128310
|